As social media grows, harmful information spreads rapidly across platforms and evolves over time, showing cross-platform and crosstemporal variations.Existing methods rely on fixed model parameters during training, which fail to handle substantial semantic discrepancies, leading to Out-Of-Distribution (OOD) problems.While test-time tuning enables dynamic parameter adjustment, it may lead to excessive adaptation to individual samples.The key challenge is how to adapt to semantic variations during testing while preventing overfitting from continuous tuning.To tackle this issue, this paper proposes RLAT, a reinforcement learning (RL)-guided adaptive tuning method for harmful text detection.First, a tuning joint optimization module is designed to update parameters and adapt to semantic variations during testing.It tunes the model by optimizing consistency loss and applying word-level attention constraints to reduce over-reliance on local words and learn a more robust global representation.Then, to mitigate overfitting caused by continuous tuning, a RL-guided adaptive decision model is introduced to direct the tuning process.It reduces the influence of local samples by selecting data and controlling parameter updates, thereby improving overall test performance.Experimental results show that the RLAT outperforms state-of-the-art baselines in cross-platform and cross-temporal scenarios across multiple public datasets.

Deep hashing is widely used in large-scale image retrieval systems due to its efficient retrieval performance. However, its susceptibility to adversarial attacks limits its security in practical applications. Adversarial training is the most effective method for improving robustness, but it often leads to a significant trade-off between robustness and retrieval accuracy. In this paper, we conduct spectral analysis and find that generating high-quality hash codes requires wide-frequency response models, whereas adversarial training forces the model into spectral collapse, degrading it to a low-frequency response model and weakening its discriminability. To address this issue, we propose a Spectral-Adaptive Adversarial Hashing (SAAH) framework, which selectively preserves discriminative and task-relevant frequency components while suppressing adversarially unstable ones, enabling robust hashing without sacrificing retrieval performance. Extensive experiments on benchmark datasets demonstrate that SAAH consistently achieves a superior balance between retrieval accuracy and adversarial robustness, achieving the best performance in both retrieval accuracy and robustness compared with existing robust hashing methods.

ABSTRACT Temporal knowledge graph reasoning (TKGR) aims to predict future facts based on events that have occurred up to the current timestamp in a temporal knowledge graph (TKG). Traditional learning‐based methods focus on structural information but overlook semantics inherent in TKGs, while rule‐based methods struggle to generate sufficient high‐quality temporal logical rules. Recently, large language models (LLMs) have been introduced into the TKGR task, typically leveraging retrieved relevant historical events and LLMs' semantic understanding and reasoning abilities to infer missing information in the query. Existing LLM‐based methods encompass both fine‐tuning and non‐fine‐tuning variants. The former demands high computational cost and exhibits limited transferability, whereas the latter suffers from performance constraints stemming from the absence of specialised adaptation training. In this paper, we introduce GT‐LLM, a novel non‐fine‐tuning TKGR framework that enhances performance by improving history retrieval quality while avoiding costly task‐specific LLM fine‐tuning and preserving flexible adaptability to different or dynamically updated TKGs. The key to our method is that it transforms a series of TKG subgraphs into a single, unified topological graph, where entities, relations, and timestamps are explicitly modelled as nodes and connected through topological edges to expose richer associations for history retrieval. Based on this unified representation, we design a general TKGR pipeline that performs history retrieval based on topological association strength and temporal relevance, followed by LLM reasoning under vocabulary‐level constraints. Furthermore, we integrate the predictions from the LLM with those from a graph‐based model to harness their complementary strengths. Extensive experimental results on standard TKGR benchmarks demonstrate that our method significantly improves history retrieval quality and achieves promising overall performance.

Over the past two decades, deep learning (DL) has achieved unprecedented breakthroughs across diverse application domains spanning computer vision (CV) to natural language processing (NLP). However, despite significant advances in computational resources and algorithmic frameworks, the training of deep neural networks continues to present formidable challenges due to persistent issues of training inefficiency and inherent data distribution biases. Recent years have witnessed the emergence of hard sample mining (HSM) as a promising paradigm to mitigate training inefficiencies and enhance model robustness through representative sample selection. Although HSM is reshaping contemporary AI research, its critical role in enabling efficient and robust model training has not yet been systematically explored. This article presents a comprehensive survey of HSM methodologies by: 1) establishing unified definitions of hard samples through rigorous sample complexity quantification criteria; 2) proposing a systematic taxonomy of HSM approaches with in-depth technical analysis; and 3) identifying pivotal research frontiers in this evolving field. This survey not only consolidates the foundations of HSM but also provides a roadmap for advancing efficient, robust, and generalizable deep learning models.

Spatiotemporal Graph Learning (SGL) under Zero-Inflated Distribution (ZID) is crucial for urban risk management tasks, including crime prediction and traffic accident profiling. However, SGL models are vulnerable to adversarial attacks, compromising their practical utility. While adversarial training (AT) has been widely used to bolster model robustness, our study finds that traditional AT exacerbates performance disparities between majority and minority classes under ZID, potentially leading to irreparable losses due to underreporting critical risk events. In this paper, we first demonstrate the smaller top-k gradients and lower separability of minority class are key factors contributing to this disparity. To address these issues, we propose MinGRE, a framework for Minority Class Gradients and Representations Enhancement. MinGRE employs a multi-dimensional attention mechanism to reweight spatiotemporal gradients, minimizing the gradient distribution discrepancies across classes. Additionally, we introduce an uncertainty-guided contrastive loss to improve the inter-class separability and intra-class compactness of minority representations with higher uncertainty. Extensive experiments demonstrate that the MinGRE framework not only significantly reduces the performance disparity across classes but also achieves enhanced robustness compared to existing baselines. These findings underscore the potential of our method in fostering the development of more equitable and robust models.

Large language models (LLMs) have achieved significant progress in mathematical reasoning, especially in elementary math. However, they remain indisposed on tackling complex questions at high-school or college levels, which put forward a more advanced requirement of mastering relevant mathematical theorems. For we humans, whether selecting the appropriate theorems according to the provided question is a crucial factor affecting the quality of the ultimate solutions, yet which has been neglected by previous research in the field of LLM reasoning. In this paper, we propose a novel approach to enhance the LLM's capability of utilizing the mathematical theorems to specific problems, which we refer to as Theorem Rationale (TR). To this end, a new dataset encompassing problem-theorem-solution triples is deliberately established for transferring principles of TR. Furthermore, we develop an evolving strategy to boost hierarchical instructions oriented on the theorems to alleviate difficulty in acquiring the curated data and facilitate the digestion of theorem application from various perspectives. Evaluations on a wide range of public datasets exhibit that the model fine-tuned with our dataset achieves consistent improvements at varying mathematical levels compared to the backbone. And further ablation studies illustrate the effectiveness of our proposed evolutionary strategies on enhancing the model's capability of math problem-solving. Overall, extensive experiments reveal the potential of our proposed method which highlights the significance of aligning the problems with the concrete theorems for LLMs to alleviate hallucination and improve the models' mathematical reasoning capabilities.

Personality identification plays important roles in understanding user behavior and offering foresight ability for downstream applications. The key challenge is how to address the scarcity of labeled personality data. Recently, some studies have adopted data augmentation and prompt learning to perform personality identification. However, they still heavily require a large amount of labeled data to learn an appropriate distance strategy, which limits the generalization and flexibility of the model. This study proposes a knowledge-enhanced hierarchical heterogeneous graph model, which adopts a global multi-view graph node encoding to acquire comprehensive personality features and their inherent associations, where three types of knowledge including part-of-speech (POS) tag, entity, and Linguistic Inquiry and Word Count (LIWC) are introduced. Then, a hierarchical heterogeneous graph with a “post-word-diverse knowledge” structure is constructed for each post to obtain enhanced representation. Finally, a relation guided representation optimization that considers intra-user relationships and inter-label relationships is further developed to learn more discriminative semantic representation. Experimental results on three widely used datasets demonstrate that the model outperforms state-of-the-art methods when training with only 100 samples (approximately 1% of the total data set).

Transformer-based language models (LMs) have demonstrated remarkable performance on many natural language tasks, yet to what extent LMs possess the capability of generalizing to unseen logical rules remains not explored sufficiently. In classical logic category, abductive, deductive and inductive (ADI) reasoning are defined as the fundamental reasoning types, sharing the identical reasoning primitives and properties, and some research have proposed that there exists mutual generalization across them. However, in the field of natural language processing, previous research generally study LMs’ ADI reasoning capabilities separately, overlooking the generalization across them. To bridge this gap, we propose UniADILR, a novel logical reasoning dataset crafted for assessing the generalization capabilities of LMs across different logical rules. Based on UniADILR, we conduct extensive investigations from various perspectives of LMs’ performance on ADI reasoning. The experimental results reveal the weakness of current LMs in terms of extrapolating to unseen rules and inspire a new insight for future research in logical reasoning.

Positional bias (PB), manifesting as nonuniform sensitivity across different contextual locations, significantly impairs long-context comprehension and processing capabilities.Previous studies have addressed PB either by modifying the underlying architectures or by employing extensive contextual awareness training.However, the former approach fails to effectively eliminate the substantial performance disparities, while the latter imposes significant data and computational overhead.To address PB effectively, we introduce Pos2Distill, a position to position knowledge distillation framework.Pos2Distill transfers the superior capabilities from advantageous positions to less favorable ones, thereby reducing the huge performance gaps.The conceptual principle is to leverage the inherent, position-induced disparity to counteract the PB itself.We identify distinct manifestations of PB under Retrieval and Reasoning paradigms, thereby designing two specialized instantiations: Pos2Distill-R 1 and Pos2Distill-R 2 respectively, both grounded in this core principle.By employing our approach, we achieve enhanced uniformity and significant performance gains across all contextual positions in long-context retrieval and reasoning tasks.Crucially, both specialized systems exhibit strong cross-task generalization mutually, while achieving superior performance on their respective tasks.

Offline meta-reinforcement learning (OMRL) enables reinforcement learning (RL) agents to adapt to unseen tasks without interacting with the environment. However, OMRL faces challenges such as Q-function overestimation and difficulties in inferring tasks correctly and robustly due to distribution discrepancy. In this paper, we introduce ConseRvative q-learning and task similarity mEAsuremenT for Offline meta-Reinforcement learning (CREATOR), a method to address these challenges using only offline datasets, without requiring additional interactions. To mitigate Q-function overestimation, we incorporate conservative Q-learning during training. We also propose a novel task similarity-based distance metric to improve the robustness of task inference. Experimental results demonstrate that the proposed CREATOR effectively reduces Q-function estimation errors, enhances task inference accuracy, and improves generalization performance across a range of challenging domains compared to existing methods.

Social media platforms has long served as open arenas where individuals discuss and change their opinions on various events, subsequently influencing the progression of these events. Public opinion, recognized as an important social signal, is instrumental in understanding the developmental patterns of social events and in guiding more informed responses. In light of this, we propose a sociologically-informed opinion prediction model, which integrates rich social interaction data with time series forecasting techniques using a graph neural network framework. This model, enriched by a sociological theoretical model, reflects the real-world dynamics of opinion evolution. Experimental results derived from three synthetic datasets and two real-world datasets indicate that incorporating user interaction data, along with more effective utilization of historical information, has led to a large improvement in the accuracy of opinion predictions. The source code and sample data for our study are available at https://github.com/RiikkaYang/SIGNN.

Board game offers a unique platform for exploring the capabilities of artificial intelligence in decision-making. It demands long-term strategic planning and opponent behaviors to refine decision-making. Since the success of AlphaGo family, learning agents have become pivotal methods for board game. However, current learning agents rarely incorporate planners or build interactive loops with opponents’ behaviors in decision-making. This paper proposes a novel planning-based model (BG-Planner) for strategic decision-making and long-term planning in board game. We propose a Graphplan-style network with alternating action and proposition layers to predict actions and assess wining rate. Further, an opponent modeling strategy is incorporated to predict opponent behaviors, assist decision-making and reduce competitive uncertainty. We also introduce a knowledge-based search tactic to enhance BG-Planner’s learning. Experimental results demonstrate that BG-Planner enhances the quality and efficiency of decision-making in the Gomoku game. It shows potential to improve deep planning strategies in decision-making intelligence.

Continual Pre-Training (CPT) has become a popular and effective method to apply strong foundation models to specific downstream tasks. In this work, we explore the learning dynamics throughout the CPT process for large language models (LLMs). We specifically focus on how general and downstream domain performance evolves at each training step, with domain performance measured via validation losses. We have observed that the CPT loss curve fundamentally characterizes the transition from one curve to another hidden curve, and could be described by decoupling the effects of distribution shift and learning rate (LR) annealing. We derive a CPT scaling law that combines the two factors, enabling the prediction of loss at any (continual) training steps and across learning rate schedules (LRS) in CPT. Our formulation presents a comprehensive understanding of several critical factors in CPT, including the learning rate, the training steps, and the distribution distance between PT and CPT datasets. Moreover, our approach can be adapted to customize training hyper-parameters to different CPT goals such as balancing general and domain-specific performance. Extensive experiments demonstrate that our scaling law holds across various CPT datasets and training hyper-parameters.

Agent-based modeling is a widely adopted approach for forecasting human opinion evolution trends. The rapid development of large language models (LLMs) introduces new perspectives to agent-based modeling, enabling sophisticated descriptions of agents’ personalities and facilitating researchers’ control over the interaction background. In this study, we propose a novel approach that leverages LLM-empowered agents to simulate the opinion distribution trends of given social issues. Our approach involves assigning agents with different personality traits based on the Five Factor Model, varying levels of background knowledge, and diverse initial opinions. We use two datasets to evaluate the performance of our framework. The results suggest that by employing our framework with appropriate prompt engineering, LLM agents can effectively forecast opinion evolution trends. The results of our framework outperform that of traditional numerical opinion dynamics methods. Our framework can effectively simulate key attributes of different social groups with agents, make agent interactions more reflective of real-world scenarios, and enable the automatic emergence of specific social psychological phenomena, such as the "backfire effect", through autonomous interactions among multiple agents.

Sarcastic remarks often appear in social media and e-commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users’ complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant’s product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model-based approach equipped with a novel hybrid multihead fusion-attention mechanism that combines self-attention, target-attention, and a feed-forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain-specific dialogue context representation, and bidirectionally enhanced sarcasm-cause pair representations are generated for detecting sarcasm spark. Experimental results on real-world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the National Natural Science Foundation of China [Grants 72293575, 62071467, and 62141608] and the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants 11500322 and 11500421]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0285 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0285 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Knowledge reasoning has been widely researched in knowledge graphs (KGs), but there has been relatively less research on hyper-relational KGs, which also plays an important role in downstream tasks. Existing reasoning methods on hyper-relational KGs are based on representation learning. Though this approach is effective, it lacks interpretability and ignores the graph structure information. In this paper, we make the first attempt at symbolic reasoning on hyper-relational KGs. We introduce rule extraction methods based on both individual facts and paths, and propose a rule-based symbolic reasoning approach, HyperPath. This approach is simple and interpretable, it can serve as a baseline model for symbolic reasoning in hyper-relational KGs. We provide experimental results on almost all datasets, including five large-scale datasets and seven sub-datasets of them. Experiments show that the expressive power of the proposed model is similar to simple neural networks like convolutional networks, but not as advanced as more complex networks such as Transformer and graph convolutional networks, which is consistent with the performance of symbolic methods on KGs. Furthermore, we also analyze the impact of rule length and hyperparameters on the model's performance, which can provide insights for future research in hypergraph symbolic reasoning.

Training deep neural networks (DNNs) on large-scale datasets is often inefficient with large computational needs and significant energy consumption. Although great efforts have been taken to optimize DNNs, few studies focused on the inefficiency caused by the data samples with less value for model training. In this article, we empirically demonstrate that sample complexity is important for model efficiency and selecting representative samples is constructive to the model efficiency. In particular, we propose hardness-aware dataset pruning method ($\gamma$-Razor) to select representative samples from large-scale datasets to remove the less valuable data samples for model training.$\gamma$-Razor is a two-stage framework that includes interclass sampling and intraclass sampling. First, we introduce the inverse self-paced learning strategy to learn hard samples and adjust their weights adaptively according to the inverse frequency of effective samples of each class. For intraclass sampling, hardness-aware cluster sampling algorithm is proposed to downsample easy samples within each class. To evaluate the performance of$\gamma$-Razor, we conducted extensive experiments on three large-scale datasets for image classification tasks. The experimental results show that models trained with the pruned datasets show competitive performances against their counterparts trained with the original large-scale datasets in terms of robustness and efficiency. Furthermore, models trained with the pruned datasets converge faster with lower energy consumption.

Accurate stock market prediction is crucial for investors seeking significant profits. With increased economic activity, various interrelations between listed companies have become important for accurate predictions. These relations can be represented as complex financial networks, aiding the development of effective graph neural network (GNN) prediction methods. However, current GNN-based methods for stock prediction typically rely on a single static network representation, which fails to capture the dynamic and multifaceted relationships inherent in financial markets. In this article, we propose the multilayer spatial–temporal graph neural network (MST-GNN) to model the complex and evolving interactions between stocks. The MST-GNN framework incorporates a novel spatial–temporal cross-layer high-order fusion mechanism, which includes two key components: spatial–temporal neighborhood aggregation and cross-layer high-order feature fusion. These components enable the model to effectively capture both the temporal evolution and cross-network feature interactions of stocks. Our extensive experiments on four stock networks from the China A-share market demonstrate that MST-GNN significantly outperforms existing GNN-based methods on stock price trend classification and return ranking tasks.

Full-complexity machine learning models, such as the deep neural network, are non-traceable black-box, whereas the classic interpretable models, such as linear regression models, are often over-simplified, leading to lower accuracy. Model interpretability limits the application of machine learning models in management problems, which requires high prediction performance, as well as the understanding of individual features’ contributions to the model outcome. To enhance model interpretability while preserving good prediction performance, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component. The first component describes the explicit feature contributions by piecewise linear approximation to increase the expressiveness of the model. The other component uses a multi-layer perceptron to increase the prediction performance by capturing the high-order interactions between features and their complex nonlinear transformations. The interpretability is obtained once the model is learned in the form of shape functions for the main effects. We also provide a variant to explore the higher-order interactions among features. Experiments are conducted on synthetic and real-world datasets to demonstrate that the proposed models can achieve good interpretability by explicitly describing the main effects and the interaction effects of the features while maintaining state-of-the-art accuracy.

Topic modeling is a commonly used text analysis tool for discovering latent topics in a text corpus. However, while topics in a text corpus often exhibit a hierarchical structure (e.g., cellphone is a sub-topic of electronics), most topic modeling methods assume a flat topic structure that ignores the hierarchical dependency among topics, or utilize a predefined topic hierarchy. In this work, we present a novel Hierarchical Deep Document Model (HDDM) to learn topic hierarchies using a variational autoencoder framework. We propose a novel objective function, sum of log likelihood, instead of the widely used evidence lower bound, to facilitate the learning of hierarchical latent topic structure. The proposed objective function can directly model and optimize the hierarchical topic-word distributions at all topic levels. We conduct experiments on four real-world text datasets to evaluate the topic modeling capability of the proposed HDDM method compared to state-of-the-art hierarchical topic modeling benchmarks. Experimental results show that HDDM achieves considerable improvement over benchmarks and is capable of learning meaningful topics and topic hierarchies. To further demonstrate the practical utility of HDDM, we apply it to a real-world medical notes dataset for clinical prediction. Experimental results show that HDDM can better summarize topics in medical notes, resulting in more accurate clinical predictions.

Interest in few-shot learning (FSL) has grown recently, but the value of feature learning, which bridges the gap between base and novel classes, remains largely understudied. The limited availability of labeled samples for each class poses a major challenge. To tackle this, we propose a simple yet effective approach called deep discriminative handcrafted feature regression (DDHFR) to explore intrinsic information and select improved discriminative features in few-shot data by mining knowledge from classical handcrafted features. To explore intrinsic information, we design several deep handcrafted feature regression (DHFR) modules and plugged them separately into different layers of the backbone to use feature engineering knowledge for feature learning optimization at different granularities. To achieve discriminative feature selection, we incorporate an auxiliary classifier (AC) into each DHFR module to enhance the acquisition of discriminative information. Furthermore, we employed self-distillation to boost ability of ACs ot be classified. Experimental results in three backbones on three datasets show that DDHFR can generally improve the performance of existing FSL methods. On average, it improves the recognition accuracy by 1.16% in two common few-shot settings.

In this paper, we investigate whether Large Language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks.Through an analysis of LLMs' internal factual recall at each reasoning step via Knowledge Neurons, we reveal that LLMs fail to harness the critical factual associations under certain circumstances.Instead, they tend to opt for alternative, shortcut-like pathways to answer reasoning questions.By manually manipulating the recall process of parametric knowledge in LLMs, we demonstrate that enhancing this recall process directly improves reasoning performance whereas suppressing it leads to notable degradation.Furthermore, we assess the effect of Chain-of-Thought (CoT) prompting, a powerful technique for addressing complex reasoning tasks.Our findings indicate that CoT can intensify the recall of factual knowledge by encouraging LLMs to engage in orderly and reliable reasoning.Furthermore, we explored how contextual conflicts affect the retrieval of facts during the reasoning process to gain a comprehensive understanding of the factual recall behaviors of LLMs.

Performing logical reasoning based on prior knowledge is a crucial human cognitive ability and has been a long-standing objective in the field of artificial intelligence. Large language models based on transformer architecture have been a common approach for logical reasoning over text. However, the current language models often struggle to learn semantic information from logical expressions, resulting in underwhelming performance on logical reasoning tasks. In this paper, we propose a novel method to convert first-order logic (FOL) expressions to the form of a graph and integrate it with embeddings from language models to enhance their reasoning ability. The proposed method is designed to learn directly from FOL formulas and is able to generalize to any scenarios involving logical expressions. Experimental results demonstrate that the proposed method enhances the model’s ability of learning logical semantic representations, and thus it brings a significant improvement on the performance of complex reasoning tasks. The code is available at https://github.com/FOL-GNN.

Many works recently have focused on computing optimal solutions for the ex ante coordination of a team for solving sequential adversarial team games, where a team of players coordinate against an opponent (or a team of players) in a zero-sum extensive-form game. However, it is challenging to directly compute such an optimal solution because the team’s coordinated strategy space is exponential in the size of the game tree due to the asymmetric information of team members. Column Generation (CG) algorithms have been proposed to overcome this challenge by iteratively expanding the team’s coordinated strategy space via a Best Response Oracle (BRO). More recently, more compact representations (particularly, the Team Belief Directed Acyclic Graph (TB-DAG)) of the team’s coordinated strategy space have been proposed, but the TB-DAG-based algorithms only outperform the CG-based algorithms in games with a small TB-DAG. Unfortunately, it is inefficient to directly apply CG to the TB-DAG because the size of the TB-DAG is still exponential in the size of the game tree and then makes the BRO unscalable. To this end, we develop our novel TB-DAG CG (DCG) algorithm framework by computing a coordinated best response in the original game first and then transforming this strategy into the TB-DAG form. To further improve the scalability, we propose a more suitable BRO for DCG to reduce the cost of the transformation at each iteration. We theoretically show that our algorithm converges exponentially faster than the state-of-the-art CG algorithms, and experimental results show that our algorithm is at least two orders of magnitude faster than the state-of-the-art baselines.

In recent years, few-shot knowledge graph completion (FKGC) emerged as a prominent research problem, focused on utilizing a limited number of reference entity pairs to complete triples with unseen relations. Recent studies have attempted addressing this problem by modeling interactions between head and tail entities. However, existing FKGC methods represent semantics predominantly based on the neighborhood information of entities in the knowledge graph, thus can only infer the hidden and unobserved relations within the knowledge graph, limiting their reasoning capabilities. To overcome these limitations, we introduce text descriptions to FKGC and propose BERT-FKGC, a model capable of learning the integrated distribution of both the entity text descriptions and neighborhood information. By using a gating network that allows the model to dynamically select weights, our method can flexibly combine neighborhood information and textual descriptions. Besides addressing the prediction of unseen relations, our method is also capable of representing unseen entities. To validate the effectiveness of our model, we introduce a new dataset, FB15K-237-One, which includes textual descriptions for entities. We conduct extensive experiments on the FB15K-237-One dataset to validate the superiority of BERTFKGC.

Few-shot, especially one-shot learning is a prominent research area in the field of knowledge graphs (KGs), aiming to utilize a limited number of triples with unseen relations as reference information for inferring missing knowledge. Recent research focuses on improving the semantic representation of entity pairs using interactions between their head and tail entities. However, this method only considers the reference information as the measurement criterion without taking into account the potential impact of it on the reasoning process of the model. In this paper, we propose a novel method that utilizes factual information interactions. Firstly, we learn static representations of entities based on their neighborhood information. Subsequently, we learn relation adaptive representations by incorporating the reference information. This interactive modeling strengthens the association between entity representations and task relations while suppressing irrelevant relations. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on two public datasets. Remarkably, on the NELL-One dataset for one-shot link prediction, our model achieves an improvement of 11.8% in MRR compared to the best baseline model.

Large language models(LLMs) have demonstrated remarkable planning and reasoning abilities, particularly as few-shot learners, when utilizing in-context learning. However, since LLMs are not grounded during training, they still encounter difficulties when acting as agents in tasks that require interaction with the environment, especially in scenarios that involve long-term and multistep interactions. Even when provided with a complete game trajectory as context, LLMs struggle to comprehend the meaning of each interaction step, and may easily hallucinate and fail. To address these challenges, we introduce the RRdE (Reasoning and Replanning during Exploration), a framework inspired by planning theories, designed for reasoning about actions and planning subgoals in complex interactive environments. The RRdE method can integrate the long-term planning ability and tooluse ability of LLMs, and transform the long-term sequential decision problem into a relatively simple reasoning problem, thereby reducing the error behavior caused by excessive context. We devise a reflection-based goal decomposition and replanning scheme, which enables the agent to overcome the strict sub-goal dependency problem caused by long-term goal planning. Consequently, RRdE achieves state-of-the-art performance in the few-shot learning setting in both AlfWorld and ScienceWorld environments, accomplishing 132 out of 134 test tasks in AlfWorld, and obtaining an average score of 82.16 in the 30 more complex and challenging scientific tasks in ScienceWorld, successfully completing 7 tasks with a full score of 100.

Online social networks have greatly facilitated the spread of information of all sorts. Meanwhile, the abundance of information in today's world also means different pieces of information will increasingly compete for people's finite attention. When different pieces of information spread together in an online social network, why would some become trendy while others fail to emerge? Existing research either models the diffusion of each piece of information independently, or fails to consider users' inactivity in online social networks. Modeling each piece of information as a meme, this paper addresses this gap by proposing a unified model for the co-diffusion of competing memes simultaneously spreading across an online social network. We are the first to identify a ubiquitous threshold for competing meme. The threshold also functions as an effective predictor that contributes to better performance in determining the outcome of meme competitions. Outcomes from this study have important implications for online campaigns and mobilizations as well as the fight against misinformation. • Unified model for competing memes' spread in online networks. • First to identify a universal meme competition threshold. • Threshold predicts meme competition outcomes effectively.

Adversarial vulnerability remains a major obstacle to the construction of reliable NLP systems. When imperceptible perturbations are added to raw input text, the performance of a deep learning model may drop dramatically under attacks. Recent work has argued that the adversarial vulnerability of a model is caused by non-robust features in supervised training. Thus, in this paper, we tackle the adversarial robustness challenge by means of disentangled representation learning, which is able to explicitly disentangle robust and non-robust features in text. Specifically, inspired by the variation of information (VI) in information theory, we derive a disentangled learning objective composed of mutual information to represent both the semantic representativeness of latent embeddings and the differentiation of robust and non-robust features. On the basis of this, we design a disentangled learning network to estimate the mutual information for realization. Experiments on the typical text-based tasks show that our method significantly outperforms the representative methods under adversarial attacks, indicating that discarding non-robust features is critical for improving model robustness.

With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a compatibility-aware graph neural network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks.

As one of the fundamental tasks of autonomous driving, depth perception aims to perceive physical objects in three dimensions and to judge their distances away from the ego vehicle. Although great efforts have been made for depth perception, LiDAR-based and camera-based solutions have limitations with low accuracy and poor robustness for noise input. With the integration of monocular cameras and LiDAR sensors in autonomous vehicles, in this article, we introduce a two-stream architecture to learn the modality interaction representation under the guidance of an image reconstruction task to compensate for the deficiencies of each modality in a parallel manner. Specifically, in the two-stream architecture, the multi-scale cross-modality interactions are preserved via a cascading interaction network under the guidance of the reconstruction task. Next, the shared representation of modality interaction is integrated to infer the dense depth map due to the complementarity and heterogeneity of the two modalities. We evaluated the proposed solution on the KITTI dataset and CALAR synthetic dataset. Our experimental results show that learning the coupled interaction of modalities under the guidance of an auxiliary task can lead to significant performance improvements. Furthermore, our approach is competitive against the state-of-the-art models and robust against the noisy input. The source code is available at https://github.com/tonyFengye/Code/tree/master .

Learning causality from large-scale text corpora is an important task with numerous applications—for example, in finance, biology, medicine, and scientific discovery. Prior studies have focused mainly on simple causality, which only includes one cause-effect pair. However, causality is notoriously difficult to understand and analyze because of multiple cause spans and their entangled interactions. To detect complex causality, we propose a self-paced contrastive learning model, namely N2NCause, to learn entangled interactions between multiple spans. Specifically, N2NCause introduces data enhancement operations to convert implicit expressions into explicit expressions with the most rational causal connectives for the synthesis of positive samples and to invert the directed connection between a cause-effect pair for the synthesis of negative samples. To learn the semantic dependency and causal direction of positive and negative samples, self-paced contrastive learning is proposed to learn the entangled interactions among spans, including the interaction direction and interaction field. We evaluated the performance of N2NCause in three cause-effect detection tasks. The experimental results show that, with the least data annotation efforts, N2NCause demonstrates competitive performance in detecting simple cause-effect relations, and it is superior to existing solutions for the detection of complex causality.

Graph representation learning is an emerging area for graph analysis and inference. However, existing approaches for large-scale graphs either sample nodes in sequential walks or manipulate the adjacency matrices of graphs. The former approach can cause sampling bias against less-connected nodes, whereas the latter may suffer from sparsity that exists in many real-world graphs. To learn from structural information in a graph more efficiently and comprehensively, this paper proposes a new graph representation learning approach inspired by the cognitive model of spreading-activation mechanisms in human memory. This approach learns node embeddings by adopting a graph activation model that allows nodes to “activate” their neighbors and spread their own structural information to other nodes through the paths simultaneously. Comprehensive experiments demonstrate that the proposed model performs better than existing methods on several empirical datasets for multiple graph inference tasks. Meanwhile, the spreading-activation-based model is computationally more efficient than existing approaches–the training process converges after only a small number of iterations, and the training time is linear in the number of edges in a graph. The proposed method works for both homogeneous and heterogeneous graphs.

Script event prediction aims to infer subsequent events given an incomplete script. It requires a deep understanding of events, and can provide support for a variety of tasks. Existing models rarely consider the relational knowledge between events, they regard scripts as sequences or graphs, which cannot capture the relational information between events and the semantic information of script sequences jointly. To address this issue, we propose a new script form, relational event chain, that combines event chains and relational graphs. We also introduce a new model, relational-transformer, to learn embeddings based on this new script form. In particular, we first extract the relationship between events from an event knowledge graph to formalize scripts as relational event chains, then use the relational-transformer to calculate the likelihood of different candidate events, where the model learns event embeddings that encode both semantic and relational knowledge by combining transformers and graph neural networks (GNNs). Experimental results on both one-step inference and multistep inference tasks show that our model can outperform existing baselines, indicating the validity of encoding relational knowledge into event embeddings. The influence of using different model structures and different types of relational knowledge is analyzed as well.

Metaphor detection is an important and challenging task in natural language processing, which aims to distinguish between metaphorical and literal expressions in text.Previous studies mainly leverage the incongruity of source and target domains and contextual clues for detection, neglecting similar attributes shared between source and target concepts in metaphorical expressions.Based on conceptual metaphor theory, these similar attributes are essential to infer implicit meanings conveyed by the metaphor.Under the guidance of conceptual metaphor theory, in this paper, we model the likeness of attribute for the first time and propose a novel Attribute lIkeness and Domain Inconsistency Learning framework (AIDIL) for word-pair metaphor detection.Specifically, we propose an attribute siamese network to mine similar attributes between source and target concepts.We then devise a domain contrastive learning strategy to learn the semantic inconsistency of concepts in source and target domains.Extensive experiments on four datasets verify that our method significantly outperforms the previous state-of-the-art methods, and demonstrate the generalization ability of our method.

Spiking Neural Networks (SNNs), also referred to as the third generation of artificial neural networks, are highly prized for their biological realism, robustness, and low power requirements. SNNs are crucial in fields such as object detection, image recognition, etc. The classification of short text plays an significant role in the development of chatbots and intent detection. It is also an important task that is widely used in many downstream tasks. However, studies applying SNNs to short text classification are limited. This paper provides a new model that uses SNNs to classify short texts. SNNs are difficult to train directly when using deep models and cannot employ large-scale language models to learn good embeddings. To resolve the challenge, we apply the character-level encoding method and convert analog neural networks into SNNs. To begin with, we represent character-level text using a temporal-and-rate joint horizontal encoding method. Then we develop a tailored deep Convolutional Neural Network (CNN) model for classifying texts. At the inference stage, we convert the tailored CNN model into an SNN model. To test the effectiveness of the proposed method, we conduct text encoding experiments on the NAMES dataset and short text classification experiments on both the 20-newsgroups dataset and the emoji-mult dataset. Experiments demonstrate that the proposed method can obtain classification accuracies that are better than or comparable to other methods.

Transformer models have been widely used in the filed of natural language processing due to their powerful learning ability. Nevertheless, recent studies have shown that transformer models are vulnerable to the maliciously crafted adversarial examples. In the challenging black box setting, main stream textual adversarial attacks typically consist of two steps: Word Importance Ranking (WIR) and word transformation. The attack performance is highly dependent on the ranking of words. Existing WIR methods are designed with heuristic rules, which lack theoretical guarantee and require a large amount of queries. To address this issue, we design a textual coalitional game and propose PWSHAP, which is a plug-and-in WIR method employing Shapley value to determine the significance of each word based on its impact on the classification. Through extensive experiments on three benchmark datasets and model architectures, we illustrate that the proposed PWSHAP achieve the-state-of-the-art attack success rate with significant fewer queries to the classification model. Meanwhile, the generated adversarial examples are more natural and coherent compared to the strong baselines.

Multimodal Named Entity Recognition (MNER) in social media posts plays an important role in both security and natural language processing domains. Existing approaches mainly include extracting useful visual features from images, and integrating them into text representation for NER via multimodal fusion. Nevertheless, there is potential correlation among samples in the dataset, but is ignored by most of the existing studies. In this paper, we propose a potential correlation-enhanced network (PCEN) for MNER. Specifically, we (1) consider the potential correlation as an important visual feature for MNER, and (2) utilize it to guide the final recognition of entities. To tackle the first issue, we employ unsupervised clustering to divide the images of training samples into clusters, and take the trainable embedding of each cluster label as a visual feature because samples with the same cluster label have higher potential correlation. To tackle the second issue, we argue that the samples in the same cluster are more likely to have similar distributions of entity types in their text. We design an inconsistency loss to encourage the consistency between the entity recognition result of each sample and the pre-trained entity type distribution of the corresponding cluster this sample belongs to. Experiments on two MNER benchmarks demonstrate the effectiveness of our proposed method.

Accurately predicting hotspot reprint paragraphs can timely provide valuable clues for topic selection, thereby improving the influence of the disseminated content. Most existing works in media reprint analysis focus on mining reprint relationships and reprint patterns. Meanwhile, few works predict the hotspot reprint paragraph from a fine-grained level. The writing style reflects the structure and semantic logic of the article to some extent. Thus, the challenge is to determine how to effectively incorporate writing style features into the semantic analysis while also reasoning deeply about the semantic relevance between sections of the article. This paper proposes a multi-perspective relevance collaborative modeling method called MPRCM-TS. It integrates writing styles of titles into the semantic representations and deeply mines the multi-perspective semantic relevance between the title and paragraphs on the basis of the attention mechanism. Simultaneously, multiple loss functions collaborate to enhance the parameter optimization ability. We evaluate the performance of the proposed model on a real-world dataset, and the experimental results demonstrate the efficacy.

Events such as crises, public opinion issues, and social hotspots usually follow certain patterns. From a large amount of historical data, we can extract these patterns to predict future events. This valuable task can be viewed as the Temporal Knowledge Graph (TKG) inference problem, as TKGs are widely employed to sketch ongoing events. However, most of the traditional TKG inference methods mainly focus only on entity prediction and do not take into consideration the variable length of information summarized from events at different periods. To address these challenges, we propose a new collaborative entity- relation prediction method called Predicting the Future Without Forgetting (PFWF). PFWF introduces historical representation to deal with the issue posed by the variability of information length. We also treat the TKG prediction task as a continual learning problem that prevents training new models from scratch when new data are added, as real-world knowledge graphs are constantly evolving. We validated the effectiveness of PFWF on four public TKG datasets related to crisis events in offline and online continual learning settings.

The COVID-19 pandemic has had a global impact on communities, economies, and healthcare systems. To control the virus's spread, numerous epidemiological investigations have been made available online, leading to a growing demand for automated tools to extract valuable information from case reports and reduce the burden on news reporters. In response to this growing need, we have meticulously curated a comprehensive data set of COVID-19 epidemiological investigation corpora, specifically designed for named entity recognition (NER) applications. This data set enables researchers and analysts to efficiently identify and extract key information from the case reports, streamlining the process of understanding and communicating the findings. To further enhance the effectiveness of NER in the context of epidemiological investigations, we evaluated and compared the performance of three cutting-edge, pre-trained model-based methods: BERT-BiLSTM-CRF, ERNIE-BiLSTM- CRF and ALBERT-BiLSTM-CRF. All techniques demonstrated impressive performance in recognizing named entities within the case reports, showcasing their potential to revolutionize the way in which epidemiological data is analyzed and disseminated. By leveraging these advanced NER techniques, we aim to facilitate more accurate and timely reporting, ultimately contributing to better-informed decision-making processes and improved public health outcomes.

Amidst the global rampage of the Coronavirus disease 2019 (COVID-19), discussions regarding COVID-19-related topics have gained significant attention from netizens worldwide. The continuous co-evolution and interaction of public sentiments towards these topics present a complex phenomenon. Uncovering the underlying causal relationships among these topics holds practical significance in promptly understanding and accurately predicting public sentiments and opinions. To address this, we present a causality-driven, co-evolutionary, and interpretable framework called Dynamic Causal Modelling and Prediction (DCMP). By detecting and quantifying the nature of causal relationships, DCMP surpasses traditional competitive methods. Moreover, the results obtained from DCMP offer comprehensive interpretations from both trend perspective and content perspective of online topics pertaining to the COVID-19 pandemic. These findings provide valuable insights for policymakers and researchers to comprehend the concealed mechanisms governing sentiment dynamics related to COVID-19 topics and devise effective policies.

Stance detection is an important research area in social media analytics and text mining and has a number of security-related applications, which aims to identify the user's viewpoint towards specific targets in domains such as social security, politics and public events. Previous research on stance detection has mainly focused on monolingual scenarios with the limited number of targets, while little attention was paid to cross-lingual stance detection. In contrast to the abundant labeled data in source language (typically English), the labeled data are often scarce in the non-English target language. Meanwhile, the diverse expressions of target further complicate the task and bring additional challenges. In this paper, we focus on cross-lingual stance detection in practical applications, where target is generally expressed as a topic and no labeled data are available for the target language. To tackle the above challenges, we propose an adversarial topic-aware memory network (ATOM) for cross-lingual stance detection. Specifically, our method first mines the generalized topic representations across source and target languages and utilizes them as the guidance to transfer knowledge from the high-resource source language to the low-resource target language. We further develop an iterative memory network to facilitate knowledge transfer across languages, which adaptively generates language-invariant topic-aware clues via adversarial training. Experimental results on three multilingual datasets in the politics domain demonstrate the effectiveness of our proposed method.

The robustness of machine learning (ML) models has gained much attention along with their wide application on various safety-required Industrial Internet of Things (IIoT) paradigms. Researchers found that some specific attacks added on sensor measurements can maliciously disturb IIoT monitors that are designed using ML architectures. The Traditional detection methods could judge whether the measurements are attacked to prevent the failure of monitors. Unfortunately, recent works argue that the commonly used detection methods could be circumvented through adaptive attacks that could acquire the mechanism of detectors; they could not truly enhance the robustness of ML models. Instead, general robust mechanisms should be performed to authentically enhance the robustness of models against any potential attacks with specific restrictions. On the basis of the above argument, we design a robust condition monitor for predicting the fault condition of IIoT systems using the adversarial training technique called robust temporal convolutional network (RTCN). The model is designed to be formally robust to attacks with restricted magnitude. The temporal convolutional network (TCN) is employed to design the base structure of the monitor. TCN can capture temporal information from sensors to enhance the feature extraction performance of models. We also present a novel false data injection (FDI) attack-generating method that utilizes the conception of adversarial perturbations to disturb well-trained monitors. The experimental results verify the efficiency of feature extraction performance of our model from IIoT systems. Furthermore, adversarial training mechanism through a min–max manner could effectively improve the reliability of ML-based IIoT monitors against strong FDI attacks.

Identifying the emotional causes of mental illnesses is key to effective intervention. Existing emotion-cause analysis approaches can effectively detect simple emotion-cause expressions where only one cause and one emotion exist. However, emotions may often result from multiple causes, implicitly or explicitly, with complex interactions among these causes. Moreover, the same causes may result in multiple emotions. How to model the complex interactions between multiple emotion spans and cause spans remains under-explored. To tackle this problem, a contrastive learning-based framework is presented to detect the complex emotion-cause pairs with the introduction of negative samples and positive samples. Additionally, we developed a large-scale emotion-cause dataset with complex emotion-cause instances based on subreddits associated with mental health. Our proposed approach was compared to prevailing CNN-based, LSTM-based, Transformer-based and GNN-based methods. Extensive experiments have been conducted and the quantifiable outcomes indicate that our proposed solution achieves competitive performance on simple emotion-cause pairs and significantly outperformed baseline methods in extracting complex emotion-cause pairs. Empirical studies further demonstrated that our proposed approach can be used to reveal the emotional causes of mental disorders for effective intervention.

BACKGROUND: Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. METHODS: Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. FINDINGS: Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. INTERPRETATION: DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.

The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method’s predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens’ participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice ($p < 0.05$) and four ($p < 0.01$) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838,$p < 0.01$). The responses to individual users’ posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.

The multimodality nature of web data has necessitated complex multimodal information retrieval for a wide range of web applications. Deep neural networks (DNNs) have been widely employed to extract semantic features from raw samples to improve retrieval accuracy. In addition, hashing is widely used to improve computational and storage efficiency. As such, deep hashing frameworks have been applied for multimodal retrieval tasks. However, there is still a great recognitive gap between primate brain structure-inspired DNNs and humans. On computer vision tasks, well-crafted DNN models can be easily defeated by invisible small attacks, and this phenomenon indicates a large recognition gap between DNN models and humans. Recently, adversarial defense methods have been shown to improve the human–machine recognition alignment in several classification tasks. However, the robustness problem on the retrieval tasks, especially on the deep hashing-based multimodal retrieval models, is still not well studied. Therefore, in this article, we present an adversarially robust training mechanism to improve model robustness for the purpose of human–machine recognition alignment on retrieval tasks. Through extensive experimental results on several social multimodal retrieval benchmarks, we show that the robust training hashing framework proposed can mitigate the recognition gap on retrieval tasks. Our study highlights the necessity of robustness enhancement on deep hashing models.

The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semantic similarity matching. Hence, executing effective privacy protection mechanisms against those retrieval software is essential for reliable social website construction. In this article, we propose a retrieval task-based adversarial perturbation generation method called Hashing Fake to meet this request. Specifically, DNNs are recently found to be vulnerable to a specific set of attacks called adversarial perturbations, which denote some magnitude-restricted signals added on objective samples to misguide well-crafted DNN models, and perturbations’ magnitudes are small enough that will not induce humans’ perception. Moreover, since existing adversarial perturbation generation methods are designed for supervised tasks, Hashing Fake constructs a differential approximation substitution for perturbation production on unsupervised retrieval tasks. Through extensive experiments on several deep retrieval benchmarks, we demonstrate that well-crafted perturbations using Hashing Fake can effectively misguide objective models’ recognitions to make false predictions. The added norm-restricted perturbations on objective samples will not alter humans’ perception; hence, Hashing Fake can be applied on real-world social websites to protect subscribers’ privacy against malicious retrieval software.

Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted k -tuple metric network (AW k -TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on k -tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AW k -TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1.

Investment decisions and risk management require understanding the time-varying dependencies between stocks. Graph-based learning systems have emerged as a promising approach for predicting stock prices by leveraging interfirm relationships. However, existing methods rely on a static stock graph predefined from finance domain knowledge and large-scale data engineering, which overlooks the dynamic dependencies between stocks. In this article, we present a novel framework called graph evolution recurrent unit (GERU), which uses a dynamic graph neural network to automatically learn the evolving dependencies from historical stock features, leading to better predictions. Our approach consists of three parts: first, we develop an adaptive dynamic graph learning (ADGL) module to learn latent dynamic dependencies from stock time series. Second, we propose a clustered ADGL (clu-ADGL) to handle large-scale time series by reducing time and memory complexity. Third, we combine the ADGL/clu-ADGL with a graph-gated recurrent unit to model the temporal evolutions of stock networks. Extensive experiments on real-world datasets show that our proposed methods outperform existing methods in predicting stock movements, capturing meaningful dynamic dependencies and temporal evolution patterns from the financial market, and achieving outstanding profitability in portfolio construction.

Multi-Label Image Classification (MLIC) appro-aches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook discriminative features and lead to model overfitting. In this study, we propose a generic framework named Parallel Self-Distillation (PSD) for boosting MLIC models. PSD decomposes the original MLIC task into several simpler MLIC sub-tasks via two elaborated complementary task decomposition strategies named Co-occurrence Graph Partition (CGP) and Dis-occurrence Graph Partition (DGP). Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels. Finally, knowledge distillation is leveraged to learn a compact global ensemble of full categories with these learned patterns for reconciling the label correlation exploitation and model overfitting. Extensive results on MS-COCO and NUS-WIDE datasets demonstrate that our framework can be easily plugged into many MLIC approaches and improve performances of recent state-of-the-art approaches. The source code is released at https://github.com/Robbie-Xu/CPSD.

The aim of cross-modal retrieval is to search for flexible results across different types of multimedia data. However, the labeled data is usually limited and not well paired with different modalities in practical applications. These issues are not well addressed in the existing works, which cannot consider the semantic information about unlabeled and unpaired data, synchronously. Self-training is a well-known strategy to handle semi-supervised problems. Motivated by the self-training, this paper proposes a self-training-based cross-modal hashing framework (STCH) to tackle the semi-supervised and semi-paired challenges. In the framework, graph neural networks are used to capture potential intra-modality and inter-modality similarities to produce pseudo labels. Then the inconsistent pseudo labels of different modalities are refined with a heuristic filter to enhance the model robustness. To train STCH, we propose an alternating learning strategy to conduct the self-train by predicting pseudo labels during the training procedure, which can be seamlessly incorporated into semi-supervised and supervised learning. In this way, the proposed method can leverage sufficient semantic information to enhance the semi-supervised effect and address the semi-paired problem. Experiments on the real-world datasets demonstrate that our approach outperforms related methods on hash cross-modal retrieval.

Performing target-oriented opinion word extraction (TOWE) from online travel reviews is a valuable reference for both tourists and attraction administration department. This paper formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus. Learning target-oriented representation accurately, locating the opinion word and extracting the complete opinion are three major challenges. Hence, we leverage aspect-based query, pos-tag and relative position and devise appropriate structure to fuse them in an encoder-decoder framework. Specifically, in the encoder, the target-fused (aspect, review) pair and the pos-tag label are encoded by transformers to model the global dependency, in the decoder, a BiLSTM is adopted to enhance contextual representation by incorporating relative position information. A real-world Chinese travel dataset for TOWE task is constructed, and the experimental results demonstrate the efficacy of the proposed model. Extensive ablation experiments are also conducted to study the effect of different components of the model.

Mining organization-related topics is helpful to analyze the information dissemination situation. Existing methods based on graph neural networks mainly consider the association between words and documents, they ignore the semantic interactions between documents, and do not consider the heterogeneity of edges which are difficult to solve the challenge of blurred topic boundaries in real scenarios, resulting in performance loss. This paper proposes a BERT-based Heterogeneous Graph Convolution Network (BERT-HGCN) approach for semi-supervised topic mining that comprehensively considers multi-semantic relations between words and documents. It deeply combines the advantages of transductive learning with pre-training models. We model documents as graph-structured data and capture multiple semantic dependencies among word-word, word-doc, and doc-doc via information propagation mechanism. During the model learning process, a two-stream encoding mechanism is used to learn the structural and semantic representations, which combines a hierarchical graph convolution network (HGCN) and a BERT-based auto-encoder. It considers both edges heterogeneity and semantics of original documents. Finally, a dual-supervision loss is used to train the classifier based on graph nodes and semantic representations for topic mining. We empirically evaluate the performance of the proposed model on a real-world organization-related dataset, and the experimental results demonstrate the efficacy of the model.

Co-movement among individual firms’ stock prices can reflect complex interfirm relationships. This paper proposes a novel method to leverage such relationships for stock price predictions by adopting inductive graph representation learning on dynamic stock graphs constructed based on historical stock price co-movement. To learn node representations from such dynamic graphs for better stock predictions, we propose the hybrid-attention dynamic graph neural network, an inductive graph representation learning method. We also extended mini-batch gradient descent to inductive representation learning on dynamic stock graphs so that the model can update parameters over mini-batch stock graphs with higher training efficiency. Extensive experiments on stocks from different markets and trading simulations demonstrate that the proposed method significantly improves stock predictions. The proposed method can have important implications for the management of financial portfolios and investment risk. Summary of Contribution: Accurate predictions of stock prices have important implications for financial decisions. In today’s economy, individual firms are increasingly connected via different types of relationships. As a result, firms’ stock prices often feature synchronous co-movement patterns. This paper represents the first effort to leverage such phenomena to construct dynamic stock graphs for stock predictions. We develop hybrid-attention dynamic graph neural network (HAD-GNN), an inductive graph representation learning framework for dynamic stock graphs to incorporate temporal and graph attention mechanisms. To improve the learning efficiency of HAD-GNN, we also extend the mini-batch gradient descent to inductive representation learning on such dynamic graphs and adopt a t-batch training mechanism (t-BTM). We demonstrate the effectiveness of our new approach via experiments based on real-world data and simulations.

Detecting product adoption intentions on social media could yield significant value in a wide range of applications, such as personalized recommendations and targeted marketing. In the literature, no study has explored the detection of product adoption intentions on social media, and only a few relevant studies have focused on purchase intention detection for products in one or several categories. Focusing on a product category rather than a specific product is too coarse-grained for precise advertising. Additionally, existing studies primarily focus on using one type of text representation in target social media posts, ignoring the major yet unexplored potential of fusing different text representations. In this paper, we first formulate the problem of product adoption intention mining and demonstrate the necessity of studying this problem and its practical value. To detect a product adoption intention for an individual product, we propose a novel and general multiview deep learning model that simultaneously taps into the capability of multiview learning in leveraging different representations and deep learning in learning latent data representations using a flexible nonlinear transformation. Specifically, the proposed model leverages three different text representations from a multiview perspective and takes advantage of local and long-term word relations by integrating convolutional neural network (CNN) and long short-term memory (LSTM) modules. Extensive experiments on three Twitter datasets demonstrate the effectiveness of the proposed multiview deep learning model compared with the existing benchmark methods. This study also significantly contributes research insights to the literature about intention mining and provides business value to relevant stakeholders such as product providers.

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic.Note to Practitioners—This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic.

In January 2021, the users of subreddit r/wallstreetbets (WSB) triggered an unprecedented short squeeze by driving up GameStop’s stock price to an unimaginable high point. During the event, a large number of users participated in the discussion about GameStop and coordinated trading behavior on r/WSB to push the stock price higher. In this article, we investigate the characteristics of the collective behaviors and social dynamics from the evolutions of topological structure, discussed topics, and user sentiment polarity (SP) by constructing dynamic interaction networks, modeling the topic, and analyzing the user sentiment. We find that the topological structure of the interaction network evolves toward a more efficient direction, the discussed topics change more centralized, and the user sentiment tends to be more positive and divergent. And we reveal that part of GameStop’s stock price is explained by the social media activity, popularity of the dominant topic, topic cohesiveness, SP of users, and sentiment divergence between interacted users on r/WSB. Our work quantitatively characterizes the interaction networks and user behavior during the GameStop short squeeze and provides an example to analyze the event which synchronously evolves in the physical space and cyberspace. It not only contributes to the analysis of social system behavior and structure but also provides valuable insights into the financial practice and policy decision-making.

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. It is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they can not provide robust performance due to the diversity of cybercrimes and can not cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.

Resilience problems from cyber-attacks on information communication technologies exist under their wide usage. False data injection (FDI) judiciously designed by attackers may cause severe consequences such as uneconomic operation and blackouts, particularly multivector energy distribution systems (MEDS), which are closely linked and interdependent. This article addresses the cyber resilient issues of an MEDS caused by FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein metric-based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, illustrating the effectiveness of the proposed cybersecured model. The produced adaptive robust economic operation for MEDS can reduce load shedding and enhance system resilience against severe cyberattacks.

Adverse event detection is critical for many real-world applications including timely identification of product defects, disasters, and major socio-political incidents. In the health context, adverse drug events account for countless hospitalizations and deaths annually. Since users often begin their information seeking and reporting with online searches, examination of search query logs has emerged as an important detection channel. However, search context - including query intent and heterogeneity in user behaviors – is extremely important for extracting information from search queries, and yet the challenge of measuring and analyzing these aspects has precluded their use in prior studies. We propose DeepSAVE, a novel deep learning framework for detecting adverse events based on user search query logs. DeepSAVE uses an enriched variational autoencoder encompassing a novel query embedding and user modeling module that work in concert to address the context challenge associated with search-based detection of adverse events. Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning auto encoders. Ablation analysis reveals that each component of DeepSAVE significantly contributes to its overall performance. Collectively, the results demonstrate the viability of the proposed architecture for detecting adverse events from search query logs.

Representation learning is a fundamental task in knowledge graph-related research and applications. Most existing approaches learn representations for entities and relations only based on static facts, where temporal information has been ignored completely. This paper aims to learn time-aware representations for entities and relations in knowledge graphs. Based on how temporal information affects the learned embeddings, we propose three assumptions and build three different models, BTS, ETS, and RTS, respectively. In these models, we build two separate embedding spaces for entities and relations, the standard translation condition is checked after projecting embedding vectors between these spaces by model-specific transformations. As to the performance, the proposed RTS model achieves state-of-the-art results in three experiments conducted on two datasets: YAGO11k and Wikidata12k, which validates the effectiveness of our model. Comparing the results of all three models, we find that relation embeddings are time-sensitive and form natural ordering, while the effects of time on entity embeddings can be safely ignored for translation-based methods. Experiments also show that our findings can be used to simplify other existing models like HyTE.

The mass media has recorded major events around the world for a long time, which is very helpful in describing the dynamic changes in all aspects of human society, including the analysis of national influence using news data. Due to the publicity and significance of mass media, the results of influence analysis must be reliable. However, the current most influence analysis methods are mainly concentrated on social media networks and cannot simply be transferred to mass media. Due to the causality as the main driving factor of influence, we introduced the causal inference method convergent cross mapping, combined with the existing general influence analysis method, proposed a credible influence analysis method in mass media. This method can filter out non-causal influences, making the results more credible. We conducted experiments on the GDELT datasets, and the results proved the effectiveness and reliability of the proposed credible influence analysis in mass media.

The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs.

COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity, non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 abstracts of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government.

Providing real-time product recommendations based on consumer profiles and purchase history is a successful marketing strategy in online retailing. However, brick-and-mortar (BAM) retailers have yet to utilize this important promotional strategy because it is difficult to predict consumer preferences as they travel in a physical space but remain anonymous and unidentifiable until checkout. In this paper, we develop such a recommender approach by leveraging the consumer shopping path information generated by radio frequency identification technologies. The system relies on spatial-temporal pattern discovery that measures the similarity between paths and recommends products based on measured similarity. We use a real-world retail data set to demonstrate the feasibility of this real-time recommender system and show that our approach outperforms benchmark methods in key recommendation metrics. Conceptually, this research provides generalizable insights on the correlation between spatial movement and consumer preference. It makes a strong case that the emerging location and path data and the spatial-temporal pattern discovery methods can be effectively utilized for implementable marketing strategies. Managerially, it provides one of the first real-time recommender systems for BAM retailers. Our approach can potentially become the core of the next-generation intelligent shopping environment in which the stores customize marketing efforts to provide real-time, location-aware recommendations.

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

Natural Language Inference (NLI) is a vital task in natural language processing.It aims to identify the logical relationship between two sentences.Most of the existing approaches make such inference based on semantic knowledge obtained through training corpus.The adoption of background knowledge is rarely seen or limited to a few specific types.In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI.KGNLI model consists of three components: a semantic-relation representation module, a knowledge-relation representation module, and a label prediction module.Different from previous methods, various kinds of background knowledge can be flexibly combined in the proposed KGNLI model.Experiments on four benchmarks, SNLI, MultiNLI, SciTail, and BNLI, validate the effectiveness of our model.

Knowledge graph completion, one of the most important research questions in knowledge graphs, aims at predicting missing links in a given graph. Current mainstream approaches adopt high-quality embeddings of entities and relations of the graph to improve their performances. However, it is not easy to devise a universal embedding learner that can fit various scenarios. In this paper, we propose a general-purpose framework which can be employed to improve the performance of knowledge graph completion. Specifically, given an arbitrary knowledge graph completion model, we first run the original model to get a ranked entity list. Then, we combine the query and the top ranked entities with attention mechanism, re-rank all these entities by feeding the combined vector into a neural network. The proposed re-ranking phase can be conveniently added to a variety of models to improve their performance without substantial modification. We conduct experiments on four datasets: WN18, FB15k, WN18RR, and FB15k-237. We choose TransE, TransH, TransD, DistMult, and ANALOGY as base models. Experiments on these datasets and models validate the effectiveness of the proposed re-ranking framework. We further explore the influence of the number of top ranked entities used in the re-ranking phase. We also test other attention mechanism to determine the most effective one, and found that vanilla attention mechanism can balance accuracy and complexity.

Label imbalance and data missing are two major challenges in the problem of credit card fraud detection. However, existing matrix completion algorithms are generally difficult and cannot be easily applied to real-world credit card fraud detection since the scale of the normally used dataset is oversized. In this paper, we develop a spectral regularization algorithm to complete the large-scale sparse matrices, and further utilize an over-sampling algorithm to tackle the problem of the imbalance between positive and negative samples. Experimental results on a real-world dataset demonstrate that our model can outperform the state-of-the-art baseline methods. The proposed method could also be extended to other large-scale scenarios where data is missing or labels are imbalanced.

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.

Online reviews are playing an increasingly important role in understanding and predicting users’ rating behavior, which brings great opportunities for users and organizations to make better decisio...

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages.

Social networks have significantly altered the behavior patterns of netizens all around the world. Therefore, accurate and expressive model of social users is increasingly demanded as it pose great value in a variety of scenarios, such as e-commerce, cyber security, and entertainment to name a few. In this paper, we propose the Pop Culture Attention Writing Model (PAWM) to explore the writing patterns of social users by explicitly capturing the influence of Internet pop culture ingredients with an attention mechanism. The writing pattern representations are learned by a memory network through storing and updating historical latent patterns. We then develop the Deep Social User Model via jointly modeling basic properties of social users, temporal contents, and the learned writing patterns based on PAWM. This paper is the first trial, to the best of our knowledge, which captures Internet pop culture information and applies deep neural network to model user writing pattern. A series of experiments conducted on social bot detection and social user identification demonstrate and validate the effectiveness of the proposed models.

Knowledge graph, or knowledge base, plays an important role in a variety of applications in the field of artificial intelligence. In both research and application of knowledge graph, knowledge representation learning is one of the fundamental tasks. Existing representation learning approaches are mainly based on structural knowledge between entities and relations, while knowledge among entities per se is largely ignored. Though a few approaches integrated entity knowledge while learning representations, these methods lack the flexibility to apply to multimodalities. To tackle this problem, in this paper, we propose a new representation learning method, TransAE, by combining multimodal autoencoder with TransE model, where TransE is a simple and effective representation learning method for knowledge graphs. In TransAE, the hidden layer of autoencoder is used as the representation of entities in the TransE model, thus it encodes not only the structural knowledge, but also the multimodal knowledge, such as visual and textural knowledge, into the final representation. Compared with traditional methods based on only structural knowledge, TransAE can significantly improve the performance in the sense of link prediction and triplet classification. Also, TransAE has the ability to learn representations for entities out of knowledge base in zero-shot. Experiments on various tasks demonstrate the effectiveness of our proposed TransAE method.

While doing summarization, human needs to understand the whole document, rather than separately understanding each sentence in the document. However, inter-sentence features within one document are not adequately modeled by previous neural network-based models that almost use only one layer recurrent neural network as document encoder. To learn high quality context-aware representation, we propose a shortcut-stacked document encoder for extractive summarization. We use multiple stacked bidirectional long short-term memory (LSTM) layers and add shortcut connections between LSTM layers to increase representation capacity. The shortcut-stacked document encoder is built on a temporal convolutional neural network-based sentence encoder to capture the hierarchical structure of the document. Then sentence representations encoded by document encoder are fed to a sentence selection classifier for summary extraction. Experiments on the well-known CNN/Daily Mail dataset show that the proposed model outperforms several recently proposed strong baselines, including both extractive and abstractive neural network-based models. Furthermore, the ablation analysis and position analysis also demonstrate the effectiveness of the proposed shortcut-stacked document encoder.

News media has become a prevalent information spreading platform, where news sites can reprint news from other sites. To better understand the mechanism of news propagation, it is necessary to model reprint behavior and predict whether a news site will reprint a piece of news. Most existing works in news reprint analysis focus on analyzing the semantic of news content, little work has been done on integrating reprint relationship among sites and news content for reprint prediction from the perspective of sites. The challenge of improving prediction performance lies in how to effectively incorporate these two kinds of information to learn a more comprehensive reprint behavior model. In this paper, we propose an Integrated Neural Reprint Prediction (INRP) model that considers both reprint relationship and news content. It models the reprint relationships as a directed weighted graph and maps it into a latent space to learn sites representations. During news content modeling process, sites representations are embedded as attention guidance to build up more site-specific content representations. Finally, sites and news representations are jointly modeled to predict whether a piece of news will be reprinted by a site. We empirically evaluate the performance of the proposed model on a real world dataset. Experimental results show that taking both the reprint relationship and news content information into consideration could allow us make more accurate analysis of reprint patterns. The mined patterns could serve as a feedback channel for both corporations and management departments.

Cognitive dissonance is a ubiquitous phenomenon which can be applied in various fields potentially. In this paper, we study cognitive dissonance through empirical analysis on social media platforms. Our study focuses on a recent “reversal event” - a topic or event experiencing a reversed development trend because of the new facts. Through statistical analysis and semantic analysis based methods, we found that (1) after the event is revised, the performance of the original followers were abnormal, which is consistent with the existence of cognitive dissonance; (2) the followers' attitude afterwards usually tended to maintain their previous behaviors. This research provides a primary building block towards the mental inference based behavior prediction for social media users, which is of great value for security related research issues.

Nowadays, it is common that a netizen creates multiple accounts across social platforms. Mapping accounts across platforms could facilitate various applications in security. Existing methods usually focus on profile and network based features. In this paper, we concentrate on capturing dynamic information of social users and present a deep dynamic user mapping model to identify the accounts across platforms. The proposed model captures dynamic latent features from three aspects including posting pattern, writing pattern, and emotional fluctuation. We also develop a matching network that fuses dynamic and traditional features to identify accounts. To the best knowledge of ourselves, this is the first trial that applies deep neural network in mapping users with dynamic information. Experiments on real world dataset demonstrated the effectiveness of the proposed method.

How people divide their attention across their friends can help to understand key issues in the realm of geopolitics. Such attention exploration allows us to compare people who focus a large portion of their attention on a small set of close friends with those disperse their attention more widely. Using 2.5 million twitter data written by 130 thousands users, we find the balance of attention is a relatively stable property of people across different modalities of interaction. It displays subtle variation across people with different characteristics and different modalities of interaction. Specifically, people's attention is more focused in mention interactions, while those active in socialization tend to allocate higher portion of total attention to their close friends. Besides external interactions, people's inner interests also affect their attention allocation. People spreading multiple memes tend to be focused, and those with more even distribution of memes are focused on their intimate friends. Finally, people's relationships also plays an important role in their attention allocation. People are more likely to focus their attention on those most like them, and this similarity sequentially enhances the intimate relationship between them.

Geopolitics is a long-lasting key issue for governments and nations to assess the international political landscape. The great proliferation of social media in recently years have provided a new avenue to make such political actions in a data driven manner. As the information consumption ability of human is limited, there demands an automatic approach to effectively identify and trace the bursts continuously emerging on social media platforms. Existing studies focusing on named entities recognition or topic detection could provide useful insights for analyzing events that are already known, yet they are incapable of identifying timely emerging trending catchphrase or topics, or memes in general.To tackle with this issue, we elaborate a framework to identify online memes and trace their future dynamics. This framework identify memes based on their independency with regard to the context, and aggregate literal variants of a same meme together into a memeplex with a newly proposed MemeMesh algorithm. Evaluation results on a large scale Twitter dataset suggest that the framework could identify geopolitical memes effectively. Further exploration on meme popularity factors reveals that popularity memes tend to generate more variants during their diffusion, and establish their dominance by attracting a large volume of active users engaging in their diffusion. Causality analysis between meme diversity and user volume suggests that high diversity of meme variants can attract more users involved in spreading a meme at the initial, but these users seldom regenerate more variants in the later time.

With the great success of neural networks, it is important to improve the information security of application systems based on them. This paper investigates a scenario where an attacker eavesdrops the intermediate representation computed by the encoder layers and tries to recover the private information of the input text. We propose a new metric to evaluate the encoder's ability to protect privacy and evaluate the Transformer-based encoder, which is the first privacy research conducted on Transformer-based neural networks. We also propose an adversarial training method to enhance the privacy of Transformer-based neural networks.

Cryptocurrency is a cutting-edge Fintech innovation and currently a worldwide hotspot. However, the high-speed evolution of it has already caused a series of public security related events all around the world. Cryptocurrency was built initially as a possible implementation of digital currency, then various derivatives were created in a variety of fields such as financial transactions, capital management, and even nonmonetary applications. This paper aims to offer analytical insights to help understand cryptocurrency by treating it as a financial asset. We position cryptocurrency by comparing its dynamic characteristics with two traditional and massively adopted financial assets: foreign exchange and stock. Based on the daily close prices about four years, we first construct the correlation matrices and asset trees of all three markets, then conduct comparisons on five properties: volatility, centrality, clustering structure, robustness, and risk. Our investigation suggests that the dynamics of cryptocurrency are more similar to stock. As to the robustness and clustering structure, our analysis shows cryptocurrency market is more fragile than stock market, thus it is currently a high-risk financial market. Our work is the first to study cryptocurrency with the help of well-understood financial assets and may shed some light on investment decisions, regulation, and legislation.

The anonymity and decentralization of Bitcoin make it widely accepted in illegal transactions, such as money laundering, drug and weapon trafficking, gambling, to name a few, which has already caused significant security risk all around the world. The obvious de-anonymity approach that matches transaction addresses and users is not possible in practice due to limited annotated data set. In this paper, we divide addresses into four types, exchange, gambling, service, and general, and propose targeted addresses identification algorithms with high fault tolerance which may be employed in a wide range of applications. We use network representation learning to extract features and train imbalanced multi-classifiers. Experimental results validated the effectiveness of the proposed method.

JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice.

Inferring drug usage patterns includes age of drug abuse and intention of rehabilitation, which is of much importance for drug abuse surveillance. The challenges are how to mine patterns from posts and interaction relationships between users. In this paper, we propose a novel drug usage pattern inference method, which improves the inference accuracy by integrating the semantic features and interaction relationships effectively. Experimental results on a real-world dataset demonstrate the efficacy of the proposed method.

This paper studied the public opinion dissemination mechanism of public health events such as avian influenza on WeChat. We collected 25,572 posts related to “avian influenza” and “H7N9” from WeChat accounts and proposed the NRT model to simulate the spread of avian influenza public opinion in WeChat. Fitting results show that it can well explain the information dissemination process and mechanism within the WeChat public account. Then the influence of model parameters on the propagation of network public opinion is further studied. Our research can provide a theoretical basis for network public opinion prediction and prevention, and has great significance for the stability of the network environment.

With the rapid development and wide application of new media, predicting the popularity of policy information on new media is of great significance for understanding and managing public opinion. However, the complexity of the diffusion patterns of policy information has brought great challenges for predicting the popularity of such information. Inspired by the methods of popularity prediction for short text information from social networks, we propose a framework for the popularity prediction of policy information. In our framework, first, the features of policy information are extracted from three dimensions: contextual information, social information and textual information. Then, effective features, such as the topic distribution, popularity competition intensity and hot information relevance, are identified by empirical analysis. Finally, the effective features are input into the prediction model to predict the popularity of policy information. We evaluate the performance of our proposed framework using a real-world dataset and the experimental results show that the framework can efficiently predict the popularity of policy information and that the features that we used are effective in improving the accuracy of policy information popularity prediction. The accurate prediction result could benefit policy makers, allowing them to make better decisions, understand and manage public opinion.

Healthcare-seeking behavior (HSB) is the motivation of formulating, developing and changing healthcare policy and medical insurance system, also the important reference of reforming and improving healthcare systems. This paper mainly focused on exploring `Patterns-Drivers' of HSB using a case study on Beijing Hand-Foot-Mouth Disease (HFMD) in China. We extracted the index for HSB and constructed the networks using the heterogeneous information from patients' records, clarified the spatial-temporal distribution of HSB and examined spatial association between HSB and socioeconomic factors using Geographically Weighted Regression. It was found that HFMD morbidity, the spatial distribution of kindergarten, the scale of the public infrastructures such as the park and the toilet was the important local drivers of HSB. The outcome based on the application of complex networks, geographic information system and spatial statistical techniques would provide valuable information supporting the optimal allocation of health resources and decision-making in disease control and prevention.

Detecting potential marketing pattern risks and preventing them can help enterprises lift operation efficiencies and reduce outlay costs. In this paper, we elaborate an ingenious method based on semi-supervised learning to identify latent marketing pattern risks for enterprises.

Nowadays, more and more sexual harassment personal stories have been shared on social media. To better monitor and analyze the extent of sexual harassment based on these social media data, we need to automatically categorize different forms of sexual harassment personal stories. Existing methods apply convolutional neural network (CNN) with different convolution window sizes to this text classification task. However, the previous CNN models do not provide an effective way to synthesize window size-related local representations, but simply concatenate all local representations together. To address this problem, we propose a new density matrix encoder, inspired by quantum mechanics, to encode local representations as particles in quantum state and generate a global representation as quantum mixed system for each story. Experiment on SafeCity dataset shows that our model outperforms CNN baseline and achieves better performance than the state-of-the-art model when considering both accuracy and speed, demonstrating the effectiveness of the proposed density matrix encoder.

The opioid epidemic has become a serious public health crisis in the United States. Social media sources such as Reddit containing user-generated content may be a valuable safety surveillance platform to evaluate discussions discerning opioid use. This paper proposes a prior knowledge based neural attention model for opioid topics identification, which considers prior knowledge with attention mechanism. Experimental results on a real-world dataset show that our model can extract coherent topics, the identified less discussed but important topics provide more comprehensive information for opioid safety surveillance.

Online user behaviors are increasingly modulated by social media. Extant literature mainly focuses on investigating how network structures affect user behaviors. However, recent empirical results demonstrate that user behaviors and network structures usually coevolve dynamically, and topological patterns turn out to be inadequate for characterizing real-world user behaviors. In this paper, we present a dynamic model to deal with this challenge. This proposed model is mainly governed by two competing principles: homophily and homeostasis. Empirical evaluations of three online real-world datasets suggest that the proposed dynamic model can well predict long-range online user behaviors.

Objective: To identify who were social media active e-cigarette users, to compare the use patterns from both survey and social media data for data triangulation, and to jointly use both datasets to conduct a comprehensive analysis on e-cigarette future use intentions. Materials and Methods: We jointly used an e-cigarette use online survey (n = 5132) and a social media dataset. We conducted analysis from 3 different perspectives. We analyzed online forum participation patterns using survey data. We compared e-cigarette use patterns, including brand and flavor types, ratings, and purchase approaches, between the 2 datasets. We used logistic regression to study intentions to use e-cigarettes using both datasets. Results: Male and younger e-cigarette users were the most likely to participate in e-cigarette-related discussion forums. Forum active survey participants were hardcore vapers. The e-cigarette use patterns were similar in the online survey data and the social media data. Intention to use e-cigarettes was positively related to e-liquid ratings and flavor ratings. Social media provided a valuable source of information on users' ratings of e-cigarette refill liquids. Discussion: For hardcore vapers, social media data were consistent with online survey data, which suggests that social media may be useful to study e-cigarette use behaviors and can serve as a useful complement to online survey research. We proposed an innovative framework for social media data triangulation in public health studies. Conclusion: We illustrated how social media data, combined with online survey data, can serve as a new and rich information source for public health research.

News diffusion prediction aims to predict a sequence of news sites which will quote a particular piece of news. Most of previous propagation models make efforts to estimate propagation probabilities along observed links and ignore the characteristics of news diffusion processes, and they fail to capture the implicit relationships between news sites. In this paper, we propose an algorithm to model the news diffusion processes in a continuous space and take the attributes of news into account. Experiments performed on a real-world news dataset show that our model can take advantage of news’ attributes and predict news diffusion accurately.

Benefitting from the development of social platforms, more and more users tend to register multiple accounts on different social networks. Linking user identities across multiple online social networks based on user behavior patterns is considerable for network supervision and information tracking. However, a user's online behavior in a social network is dynamic. The user profile may be changed due to some specific reasons such as user migration or job changes. Thus, catching the dynamics of evolutionary user data and collecting the latest user features are important and challenging issues in the area of user identity linkage. Inspired by deep learning models such as word2vec and Deep Walk, this paper proposes an integrated framework to catch the dynamic user data by supplementing vacant features and updating outdated features in data sources. The framework firstly represents all textual and structural user data into Iow- dimensional latent spaces by utilizing word2vec and DeepWalk, then, integrates different user features and predicts vacant data fields based on late fusion approach and cosine similarity computation. We then explore and evaluate the application of our proposed method in a user identity mapping task. The results proved that our framework can successfully catch the dynamic user data and enhance the performance of identity linkage models by supplementing and updating data sources advance with the times.

Exploring user stances and attitudes is beneficial to a number of Web related research and applications, especially in social media platforms such as Twitter. Stance detection in Twitter aims at identifying the stance expressed in a tweet towards a given target (e.g., a government policy). A key challenge of this task is that a tweet may not explicitly express opinion about the target. To effectively detect user stances implied in tweets, target content information plays an important role. In previous studies, conventional feature-based methods often ignore target content. Although more recent neural network-based methods attempt to integrate target information using attention mechanism, the performance improvement is rather limited due to the underuse of this information. To address this issue, we propose an endto- end neural model, TGMN-CR, which makes better use of target content information. Specifically, our model first learns conditional tweet representation with respect to specific target. It then employs a target-guided iterative process to extract crucial stance-indicative clues via multiple interactions between target and tweet words. Experimental results on SemEval-2016 Task 6.A Twitter Stance Detection dataset show that our proposed method outperforms the state-of-the-art alternative methods, and substantially outperforms the comparative methods when a tweet does not explicitly express opinion about the given target.

Social media platforms make the spread of social event information quicker and more convenient. Some of these social events may become hot topics, which highlights the importance of event popularity prediction in public management, decision making and other security related applications. Due to the complexity of social event itself, it has two unique characteristics which most previous popularity prediction work has ignored: (1) the discussion of an event itself may consist of several components, e.g. different sub-events, different stances or different user communities; (2) the popularity of an event can be influenced by other related events. To address its unique characteristics, we propose an event popularity prediction model combining partition and interaction. We employ reinforcement learning to automatically partition an event into components and recognize related events. Then we predict event popularity by modeling component information and interactions between related events. Experimental results on a real world dataset show that our proposed model can outperform the competitive baseline methods.

Cryptocurrency is a rapid developing financial technology innovation which has attracted a large number of people around the world. The high-speed evolution, radical price fluctuations of cryptocurrency, and the inconsistent attitudes of monetary authorities in different countries have triggered panic and chain reactions towards the application and adoption of cryptocurrency and have caused public security related events. So far, a lot of researches and analyses have focused on just one or only a few number of cryptocurrencies, a comprehensive analysis of the whole cryptocurrency market and its systemic risk is still lacking. In this paper, we analyze the dynamics and systemic risk of the cryptocurrency market based on the public available price history. We first validated that the correlation matrix and asset tree are good tools to analyze the risk and stability of the cryptocurrency market. Furthermore, consistent with public perception, our quantitative analysis reveals that the cryptocurrency market is relatively fragile and unstable. Our work is the first to investigate the systemic risk of the whole cryptocurrency market and may shed some light on cryptocurrency related investment decision, regulation, and legislation.

Knowledge graph plays an important role in detection, prediction, early warning, and other security related applications. A fundamental task in applying knowledge graph is the so-called multi-hop reasoning, which focuses on inferring new relations between entities. In this paper, we introduce attention mechanism to the classic compositional method. After finding reasoning paths between entities, we aggregate these paths' embeddings into one according to their attentions, and infer the relation of entities based on the combined embedding. Two experiments on NELL-995 dataset, fact prediction and link prediction, validated that our method outperforms all baselines.

Objective: Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Methods: Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Results: Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed. Conclusion: Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications.

Bots are regarded as the most common kind of malwares in the era of Web 2.0. In recent years, Internet has been populated by hundreds of millions of bots, especially on social media. Thus, the demand on effective and efficient bot detection algorithms is more urgent than ever. Existing works have partly satisfied this requirement by way of laborious feature engineering. In this paper, we propose a deep bot detection model aiming to learn an effective representation of social user and then detect social bots by jointly modeling social behavior and content information. The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior. As to the representation of content, we regard the user content as temporal text data instead of just plain text as be treated in other existing works to extract semantic information and latent temporal patterns. To the best of our knowledge, this is the first trial that applies deep learning in modeling social users and accomplishing social bot detection. Experiments on real world dataset collected from Twitter demonstrate the effectiveness of the proposed model.

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and spreading platform, where users can retweet interesting messages. To better understand the propagation mechanism for information diffusion, it is necessary to model the user retweeting behavior and predict future retweets. Some existing work in retweeting prediction based on matrix factorization focuses on using user-message interaction information, user information and social influence information, etc. The challenge of improving prediction performance is how to jointly perform deep representation of these information to solve the sparsity problem and then learn a more comprehensive retweeting behavior model. Inspired by word2vec and co-factor matrix factorization model, this paper proposes a hybrid model, called HCFMF, for learning users' retweeting behavior, it first computes the message content similarity by considering the message co-occurrence, the author information and word2vec based low-dimensional representation of content, then, jointly decomposes the user-message matrix and message-message similarity matrix based on a co-factorization model. We empirically evaluate the performance of the proposed model on real world weibo datasets. Experimental results show that taking the dense representation of author and content information into consideration could allow us make more accurate analysis of users' retweeting patterns. The mined patterns could serve as a feedback channel for both consumers and management departments.

Predicting meme burst is of great relevance to develop security-related detecting and early warning capabilities. In this paper, we propose a feature-based method for real-time meme burst predictions, namely “Semantic, Network, and Time” (SNAT). By considering the potential characteristics of bursty memes, such as the semantics and spatio-temporal characteristics during their propagation, SNAT is capable of capturing meme burst at the very beginning and in real time. Experimental results prove the effectiveness of SNAT in terms of both fixed-time and real-time meme burst prediction tasks.

Public sentiment permeated through social media is usually regarded as an important measure for public opinion monitoring, policy making, and so forth. However, the deluge of user-generated content in web, especially in social platform, causes great challenge to public sentiment analysis tasks. Therefore, Web-derived Emotional Word Detection (WEWD) is proposed as a fundamental tool aims to alleviate this problem. Most previous works on WEWD focus on rules, syntax, and sentence structures, a few utilize semantic information which has the potential to further increase the accuracy and efficiency of WEWD. In this paper, we propose a Global-Local Latent Semantic (GLLS) framework for WEWD to make a full use of latent semantic information with the help of multiple sense word embedding technology. We devise two computational WEWD models, called Ensemble GLLS (EGLLS) and Deep GLLS (DGLLS). EGLLS exploits an ensemble learning way to fuse the global and local latent semantics while DGLLS takes advantage of deep neural network. We also design an old-new corpus enrich technique to help increase the effectiveness of the overall training and detecting process. To the best of our knowledge, this is the first work which applies multiple sense word embedding and deep neural network in WEWD related tasks. Experiments on real datasets demonstrate the effectiveness of the proposed idea and methods.

Social bots are regarded as the most common kind of malwares in social platform. They can produce fake messages, spread rumours, and even manipulate public opinions. Recently, massive social bots are created and widely spread in social platform, they bring negative effects to public and netizen security. Bot detection aims to distinguish bots from human and it catches more and more attentions in recent years. In this paper, we propose a behavior enhanced deep model (BeDM) for bot detection. The proposed model regards user content as temporal text data instead of plain text to extract latent temporal patterns. Moreover, BeDM fuses content information and behavior information using deep learning method. To the best of our knowledge, this is the first trial that applies deep neural network in bot detection. Experiments on real world dataset collected from Twitter also demonstrate the effectiveness of our proposed model.

Cyber-trafficking is the illegal transport of humans, drugs, weapons, or goods by means of Internet-enabled electronic devices. Currently, there is a lack of surveillance and understanding of the rapidly growing social concern about cyber-trafficking (CT). This paper describes the Cyber-Trafficking Surveillance System (CyTraSS) and provides preliminary findings of using the system to monitor CT social media discussions. CyTraSS supports flexible collection, analysis, and visualization of social media content, user linkage, and temporal features. The CyTraSS database contains a focused collection of over 2,318,691 social media messages posted by over 740,070 users who discussed about trafficking crimes and issues. CyTraSS supports keyword search, sentiment analysis, message statistics summarization, and influential leader identification. Emotion expressed in social media messages is extracted and aggregated quantitatively to indicate community mood. We examined three use cases about a sex trafficker identified by a flight attendant, Federal use of private prisons, and trafficking cases related to Beijing. These time-sensitive incidents are highly-relevant to CT and were identified by using clues provided by CyTraSS. The results have strong implications for understanding CT concern on social media.

Public sentiments affecting health outcomes are increasingly modulated by social media. Existing literature mainly focus on investigating how network structure affects the contagion of health sentiments. However, most of these studies neglect that the interaction topology change in time. In fact, the change of inter-individual connections over time is associated with individual attributes. The mechanism through which individual attributes reshapes the connection topology is mainly governed by the competition between two principles, i.e., homophily (establishing or reinforcing social connections) and homeostasis (preserving the total strength of social connections to each individual). No existing approaches are yet able to accommodate these two competing effects at the same time. We thus propose a new statistical model (H2 model, Homophily and Homestasis model) to depict the evolution of temporal network, which is governed by the competition of homophily and homeostasis. In addition, we consider the mediation effect of external shock events, which enables us to separate exogenous confounding factors. Evaluation on Twitter data suggests that H2 model can capture long-range sentiment dynamics and external shock events. In sentiment prediction, H2 consistently outperforms existing methods in terms of error rate. Through the model's shock tensor, we successfully detect several typical events, and reveal that users in negative emotions are more influenced by external shock events than those with positive emotions. Our findings have practical significance for those who supervise and guide health sentiments in online communities.

Online emotion diffusion is a compound process that involves interactions with multiple modalities. For instance, different behaviors influence the velocity and scale of emotion diffusion in online communities. Depicting and predicting massive online emotions helps to guide the trend of emotion evolution, thus avoiding unprecedented damages in crises. However, most existing work tries to depict and predict online emotions based on models not considering related modalities. There still lacks an efficient modeling framework that promotes performance by leveraging multi-modality knowledge, and quantifies the interactions among different modalities. In this paper, we elaborate a computational model to jointly depict online emotions and behaviors. By introducing a common structure, we can quantify how user emotions interact with the corresponding behaviors. To scale up to large dataset, we propose a hierarchical optimization algorithm to accelerate the convergence of the model. Evaluation on Sina Weibo dataset suggests that prediction error rate is lowered by 69 percent with the proposed model. In addition, the proposed model helps to explain how user emotions influence consequent behaviors in extreme situations.

The competitive perspective implied in online texts reflect people's conflicts in their stances and viewpoints. Competitive perspective identification aims to determine people's inclinations to one of multiple competitive perspectives, which is an important research issue and can facilitate many security-related applications. As the word usage of different perspectives is distinct in various topics, in this paper, we first proposes a supervised topic-refined method for competitive perspective identification. Our method refines perspective classifiers with the document-topic distributions mined from texts. To reduce human labor in data annotation, we further extend our work in a semi-supervised manner and propose a user-based bootstrapping framework. As the perspectives people hold are relatively stable, our bootstrapping process leverages the user-level perspective consistency to select high-quality classified texts from unlabeled corpus and boost the perspective classifier iteratively. Experimental studies show the effectiveness of our proposed approach in identifying the competitive perspectives of online texts.

With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.

Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.

Monitoring the evolution phases of real-time event including occurrence, development, climax, decline and ending is crucial for management department to intuitively and comprehensively understand the event and then make better decisions. However, there have been very few studies on performing phase evolution analysis of event using the number of posts at the specific time unit. The challenge of this problem is how to identify temporal pattern and mine topic of different phases automatically. In this paper, we propose a unified phase evolution mining model, it firstly identifies the temporal patterns of phases based on k-means and empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media platform to understand the event evolution in a more comprehensive way. Experimental results show the characteristics of event evolution on different social media platforms and verify the efficacy of the proposed model.

Online advertisers bidding in sponsored search auctions through Web search engines are experiencing unprecedentedly fierce competition in recent years, resulting in an obvious upward trend in bids submitted by advertisers. This bid inflation phenomenon poses significant threat to the overall stability and the effectiveness of sponsored search markets. Existing research efforts, however, do not yet provide directly relevant theoretical or managerial insights to help understand this kind of real-world competitive bidding behavior in sponsored search. Our research is targeted at filling in this important research gap. Based on a model of advertisers' rational competitive preference, we propose a novel equilibrium solution concept called the upper bound Nash equilibrium (UBNE), which can be viewed as the upper bound of the output-truthful subset in the NE continuum of sponsored search auctions. We show that the UBNE can better characterize advertisers' competitive bidding behavior than other solution concepts, offering a viable theory driven behavioral explanation for bid inflation. We also show that the UBNE is a stable outcome of competitions among advertisers in repeated game settings and yields the optimal outcome for Web search engines.

In this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well.

Mining topics in short texts (e.g. tweets, instant messages) can help people grasp essential information and understand key contents, and is widely used in many applications related to social media and text analysis. The sparsity and noise of short texts often restrict the performance of traditional topic models like LDA. Recently proposed Biterm Topic Model (BTM) which models word co-occurrence patterns directly, is revealed effective for topic detection in short texts. However, BTM has two main drawbacks. It needs to manually specify topic number, which is difficult to accurately determine when facing new corpora. Besides, BTM assumes that two words in same term should belong to the same topic, which is often too strong as it does not differentiate two types of words (i.e. general words and topical words). To tackle these problems, in this paper, we propose a non-parametric topic model npCTM with the above distinction. Our model incorporates the Chinese restaurant process (CRP) into the BTM model to determine topic number automatically. Our model also distinguishes general words from topical words by jointly considering the distribution of these two word types for each word as well as word coherence information as prior knowledge. We carry out experimental studies on real-world twitter dataset. The results demonstrate the effectiveness of our method to discover coherent topics compared with the baseline methods.

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and seeking channel for health surveillance, in which users form interactive networks by posting and replying messages, providing and rating reviews, attending multiple discussion boards on health-related topics. Users' behaviors in these interactive networks reflect users' multiple interests. To provide better information service for users, it is necessary to analyze the user interactions and predict users' multi-interests. Most existing work in predicting users' multi-interests based on multi label network classification focuses on using approximate inference methods to leverage the dependency information to improve classification results. Inspired by deep learning techniques, DEEPWALK learns label independent latent representations of vertices in a network using local information obtained from truncated random walks, which provides an efficient way for predicting users multi-interests from user interactions. In this paper, we develop a user's multi-interests prediction model based on DEEPWALK, weight information of user interactions is considered when modeling a stream of short constrained random walks and SkipGram is employed to generate more accurate representations of user vertices, which help identify users' interests. Experimental results on two real world health-related datasets show the efficacy of the proposed model.

Topic modeling is a popular text mining technique for extracting latent semantics from text. It can be widely applied in intelligence analyzing, anti-terrorist, and various other security related tasks. Most existing topic models only focus exclusively on the text literally, and disregard rich contextual, cultural, and language background, hindering the understanding and discovering of the key clues implied in the text. Based on cognitive psychology theories, we justify the classical psychological activation theory named Adaptive Control of Thought from the perspective of information theory. Then, we propose a fast and loosely-coupled activation presentation of text for topic models. Our method mimics the aspect of human cognitive procedure when facing the activation of new concepts based on word correlations and word frequencies. Experimental results on multiple tasks show that our activation presentation models can significantly improve the performance of the topic models with linear time consumption.

In this paper, we propose a new role analyzing paradigm for social networks enlightened by topic modeling, which can be adopted as a primitive building block in various security related tasks, such as hidden community finding, important person recognizing and so on. We first present the social network under analyzing as a heterogeneous network constructed by both the users and the subjects discussed among them. We then view this network in a Bag-of-Users schema, which mimics its classical Bag-of-Words counterpart. In this schema, the subjects discussed are treated as “documents” while the users are treated as “words” which construct the “documents”. Based on this novel presentation, we finally apply topic modeling technology to perform the social role clustering. Experiments on a practical security-related social network dataset prove the effectiveness of our approach.

Public sentiment permeated through social media is usually regarded as an important measure for hot event detecting, policy making and so forth, hence many governments and intelligence agencies have been launching various initiatives to facilitate theories, technologies and systems toward monitoring its fluctuation. Recently, massive new words are created and widely spread in social media, and they pose a great influence on sentiment analysis. Facing this situation, most previous work still just add those new words into sentiment lexicon, none of the existed researches focuses on the role and influence of new words in emotional expression. In this paper, we pay more attention to the influence of new words and propose two novel new words based sentiment analysis methods, named NWLb and NWSA, the former only with the help of lexicon and the latter further incorporates machine learning, which utilize the distinctive role of new words to improve the effectiveness of sentiment analysis in social media. Experiments on real social media dataset demonstrate the effectiveness and performance of our methods.

The proliferation of social media has increased the competition among different memes, which can be free texts, trending catchphrases, or micro media. As human attention is limited, these memes compete with each other, and go in and out of popularity at a rapid pace, sometimes even faster than we can recognize. Popular memes often shape the mindsets of online communities, and also shed light on their future tendencies. Considering the huge volume of memes generated and their continuous mutations, extracting and tracing online memes automatically is rather challenging. In this paper, we propose an automatic meme extraction algorithm. The proposed algorithm extracts massive memes based on phrases independency, and clusters phrase variants of a single meme efficiently. Evaluation on measles outbreak in the USA in 2015 indicates that the proposed algorithm could extract typical memes reflecting the fierce campaign between the pro-vaccination community and the anti-vaccination community. In both communities, memes are power-law distributed, and popular ones have many variants that appear more frequently. By tracing the evolution of online memes, we uncover that popular memes converge and generate peaks at times. Though the pro-vaccination community and the anti-vaccination community may focus on similar memes, they comprehend memes from totally different perspectives and deliver opposing opinions of measles vaccination.

People write online documents from different personal perspectives. The competitive perspectives they hold reflect the conflicts in their fundamental stances and viewpoints. For many security-related applications, it is both beneficial and critical to identify the competitive perspectives implied in online documents. Previous work on competitive perspective identification is based on word features, which did not consider that the word usage for perspective expression varies with topics in documents. Thus topic information can be incorporated and contribute to a more fine-grained treatment of perspective identification. Motivated by this, this paper proposes an approach for competitive perspective identification in online documents via topic based refinement. Our approach refines the basic word feature-based perspective identification model with latent semantic information. In addition, we develop a self-adaptive process to fit the model parameters automatically. Experimental study shows the effectiveness of our approach compared to the related work and the baseline methods.

Illicit trade in tobacco products (ITTP) would severely disrupt the market order, greatly threaten citizen's health, and damage the interests of the nation and the consumers, which has attracted great attention of tobacco monopoly administration. Quick and correct detection of the changes and the drivers of ITTP activity would be very significant to the surveillance, tracing, early warning, prediction, prevention and control of the illicit tobacco trade, which are very important challenges for tobacco monopoly administrations in China. In this paper, we introduce spatial-temporal analysis techniques into detecting the spatial-temporal patterns and drivers of ITTP based on the dataset provided by tobacco monopoly administration of Changsha county in Hunan province, China. The results suggested that ITTP in the county mostly occurred along the downtown-county borders, or nearby the toll stations located on the highway, logistics and freight distribution center and the junction of neighboring areas. Positive correlations were found between illegal rate and population density, the number of kindergartens and nursery schools, proximity to the borders, the number of the pupils and the middle school students, which was consistent with the previous study and social etiquette. This study could provide important intelligence and clues for the decision makers and make sure that the resources should be allocated as effectively as possible.

Review rating prediction is of much importance for sentiment analysis and business intelligence. Existing methods work well when aspect-opinion pairs can be accurately extracted from review texts and aspect ratings are complete. The challenges of improving prediction accuracy are how to capture the semantics of review content and how to fill in the missing values of aspect ratings. In this paper, we propose a novel review rating prediction method, which improves the prediction accuracy by capturing deep semantics of review content and alleviating data missing problem of aspect ratings. The method firstly learns the latent vector representation of review content using skip-thought vectors, a state-of-the-art deep learning method, then, the missing values of aspect ratings are filled in based on users? history reviewing behaviors, finally, a novel optimization framework is proposed to predict the review rating. Experimental results on two real-world datasets demonstrate the efficacy of the proposed method.

Summary Recent years have witnessed the emergence of new types of semantic search engines which attempt to overcome the defects of the traditional search engines by providing different search patterns. A big question here is that in order to achieve the semantic search engines (SSEs), what type(s) of search patterns should SSEs support? To help seek one of the many possible answers, in this paper we start with classifying and comparing current search engines, particularly from the perspective of search patterns which consist of index structure, user profiles, and interaction mechanism. We then present a novel search pattern named ExNa by defining its model and basic operations in detail. To validate the ExNa search pattern, we develop a prototype search engine named KNOWLE, and the experimental results show that KNOWLE equipped with ExNa can improve both the efficiency of the entire system when compared with search engines of other search patterns. Copyright © 2016 John Wiley & Sons, Ltd.

The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, tags, etc.). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.

Market surveillance systems (MSSs) are increasingly used to monitor trading activities in financial markets to maintain market integrity. Existing MSSs primarily focus on statistical analysis of market activity data and largely ignore textual market information, including, but not limited to, news reports and various social media. As suggested by both theoretical explorations in finance and prevailing market surveillance practice, unstructured market information holds major yet underexplored opportunities for surveillance. In this paper, we propose a news analysis approach with the help of commonsense knowledge to assess the risk of suspicious transactions identified in market activity analysis. Our approach explicitly models semantic relations between transactions and news articles and provides semantic references to words in news articles. We conducted experiments using data collected from a real-world market and found that our proposed approach significantly outperforms the existing methods, which are based on transaction characteristics or traditional textual analysis methods. Experiments also show that the performance advantage of the proposed approach mainly comes from the modeling of news-transaction relationships. The research contributes to the market surveillance literature and has significant practical implications.

Social media provide opportunities for policy makers to gauge pubic opinion. However, the large volumes and variety of expressions on social media have challenged traditional policy analysis and public sentiment assessment. In this article, we describe a framework for social‐media‐based public policy informatics and a system called “iMood” that addresses the needs for sentiment and network analyses of U.S. immigration and border security. iMood collects related messages on Twitter, extracts user sentiment and emotion, and constructs networks of the Twitter users, helping policy makers to identify opinion leaders, influential users, and community activists. We evaluated the sentiment, emotion, and network characteristics found in 909,035 tweets posted by over 300,000 users during three phases between May and November 2013. Statistical analyses reveal significant differences in emotion and sentiment among the 3 phases. The Twitter networks of the 3 phases also had significantly different relationship counts, network densities, and total influence scores from those of other phases. This research should contribute to developing a new framework and a new system for social‐media‐based public policy informatics, providing new empirical findings and data sets of sentiment and network analyses of U.S. immigration and border security, and demonstrating a general applicability to different domains.

Emotion plays an important role in shaping public policy and business decisions. The growth of social media has allowed people to express their emotion publicly in an unprecedented manner. Textual content and user linkages fostered by social media networks can be used to examine emotion types, intensity, and contagion. However, research into how emotion evolves and entrains in social media that influence security issues is scarce. In this research, we developed an approach to analyzing emotion expressed in political social media. We compared two methods of emotion analysis to identify influential users and to trace their contagion effects on public emotion, and report preliminary findings of analyzing the emotion of 105,304 users who posted 189,012 tweets on the U.S. immigration and border security issues in November 2014. The results provide strong implication for understanding social actions and for collecting social intelligence for security informatics. This research should contribute to helping decision makers and security personnel to use public emotion effectively to develop appropriate strategies.

Emotion entrainment accounts for the rhythmic convergence of human emotions through social interactions. This phenomenon abounds in various disciplines, i.e. effervescency in soccer games, anger proliferation in violence incidents, or anxiety diffusion in disasters. Although emotion entrainment is highly relevant to the quality of human daily life, the principles underpinning this phenomenon is still unclear. Previous dynamic models try to explain entrainment phenomenon by assuming symmetrical coupling among identical individuals. Yet this assumption clearly does not hold in real-world human interactions. As such, we propose an alternative model that captures asymmetric relationships. In depicting the coupling mechanism, the effect of social influence is also encoded. Experimental results on two emergent social events suggest that the proposed model characterizes emotion trends with high accuracy. Also, we explain the emotion dynamics by analyzing the reconstructed entrainment matrix. Our work may present practical implications for those who want to guide or regulate the emotion evolution in emergency events discussed online.

Weibo has become an important information sharing platform in our daily life in China. Many applications utilize Weibo data to analyze hot topic and opinion evolution patterns to gain insights into user behavior. However, various spam messages degrade the performance of these applications and thus are essential to be filtered. In this paper, we propose a unified spam detection approach, which utilizes external knowledge sources to expand keywords features and applies an ensemble under-sampling based strategy to handle the class-imbalance problem. The experimental results show the effectiveness and robustness of our approach in Weibo data.

Existing causal inference methods for social media usually rely on limited explicit causal context, preassume certain user interaction model, or neglect the nonlinear nature of social interaction, which could lead to bias estimations of causality. Besides, they often require sufficiently long time series to achieve reasonable results. Here we propose to take advantage of multivariate embedding to perform causality detection in social media. Experimental results show the efficacy of the proposed approach in causality detection and user behavior prediction in social media.

Revealing underlying social influence among users in social media is critical to understanding how users interact, on which a lot of security intelligence applications can be built. Existing methods fail to take into account the interaction relationships among memes. In this paper, we propose to simultaneously model social influence and meme interaction in information diffusion with novel multidimensional Hawkes processes. Experimental results on both synthetic and real world social media data show the efficacy of the proposed approach.

One critical operational decision facing online advertisers when they engage in sponsored search advertising is concerned with the allocation of a limited advertising budget. In particular, dealing with multi-keyword search markets over multiple decision periods poses significant decision-making challenges. In this paper, we develop a novel budget allocation optimization model with multiple search advertising markets and a finite time horizon. One key element of our modeling work is developing a customized advertising response function when considering distinctive features of sponsored search, including the quality score and the dynamic advertising effort. We derive a feasible solution to our budget model and study its properties. Computational experiments are conducted on real-world data to evaluate our budget model and perform parameter sensitivity analysis. Experimental results indicate that our budget allocation strategy significantly outperforms several baseline strategies. In addition, the identified properties derived from the solution process illuminate critical managerial insights for advertisers in sponsored search.

Quality score (QS) plays a critical role in sponsored search advertising (SSA) auctions, and in practice is closely correlated to the historical click-through rate (CTR) of an advertisement. The CTR-QS correlation may impose great influence on advertisers' positioning strategies of selecting the targeting slots in the sponsored list. In the literature, however, QS is implicitly assumed to be an independent variable and exogenously assigned by Web search engines, so that little theoretical or managerial insights can be offered to help understand the positioning dynamics in SSA auctions with CTR-QS correlation. We strive to bridge this research gap in this paper. Based on a discrete time-dependent optimal control model, which explicitly captures the relationship between the historical CTR and QS, we determine the optimal strategy for revenue-maximizing advertisers' QS-based positioning decisions through a policy-iteration-based numerical approximation method. We also investigate two practically-used heuristic strategies, namely the greedy and farsighted positioning strategies, aiming to examine and help understand advertisers' real-world positioning dynamics. Our analysis indicates that both the optimal and greedy positioning strategies lead advertisers to monotonically increase or decrease their targeting slots over time, which may cause a polarization trend emerging in SSA markets. Meanwhile, the farsighted positioning strategy can accelerate the polarization. Our simulations show that both the greedy and farsighted strategies have good revenue performance. Our findings indicate that advertisers should monotonically adjust their targeting positions to maximize their revenue in CTR-QS correlated SSA auctions. Our findings also highlight the need for Web search engine companies to set a lowered weight for historical CTRs or use position-normalized CTRs in their QS measurements, so as to suppress the polarization trend.

The sociological theory of entrainment accounts for the synchronization of human rhythmic modalities through social interactions: they coordinate in a variety of dimensions including linguistic styles, facial expressions, music pace, applause, and so on. Though highly relevant, emotion entrainment has received little attention to date. In addition, most previous studies on entrainment are done through small scale or controlled laboratory studies. In this paper, we investigate emotion entrainment in the context of online social media. To the best of our knowledge, this is the first time that emotion entrainment has been examined on a large scale, real world setting. For this purpose, we propose a framework that can model entrainment phenomenon and measure its effect. Our framework differentiates from previous research by its model-free essential and discerning in entrainment directions. These traits enable us to model entrainment dynamics under few assumptions, and distinguish emotion flow of entrainment. In our studies, we investigate entrainment patterns under different emotion states, i.e. positive, neutral and negative. We discover that entrainments under different emotions all follow a power law distribution. Besides, people are willing to entrain to others under positive emotion, and users with positive emotion are more likely to be entrained. By inspecting the interactions between entrainment and emotion, we reveal that entrainment has an effect of negotiating different emotion types toward an even distribution.

With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions inCQAcommunities, it is necessary to analyze the community structures and track community evolution over time. Existing work inCQAfocuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns inCQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision‐making basis for business.

Action knowledge is an important type of behavioral knowledge and of vital importance to many applications in social computing, especially in behavior modeling, analysis and prediction. In this paper, we present a computational method to action knowledge extraction from online media. Our approach is based on mutual bootstrapping and combined with knowledge reasoning. Compared with the related work, our approach can acquire more types of action knowledge, and needs much less human labor. We evaluate the performance of our method using the Web textual data from security informatics domain. The experimental results show the effectiveness of our proposed method.

Extracting emotions from online reviews is crucial to many security-related applications as well as applications in other domains. Traditional approaches to emotion extraction have mainly focused on mining the polarities of opinions or using annotated data to extract emotion types. Emotion theories, which identify the underlying cognitive structure and emotional dimensions that are key to generate emotions, have almost been totally ignored in previous work. To facilitate the automatic extraction of emotions from textual data, in this paper, we propose an emotion model based approach to emotion extraction from online reviews. Informed by the widely used OCC emotion model, we employ a statistical method to extract emotion words with their dimension values from texts, and implement OCC model to obtain emotions based on the emotion-dimension dictionary. We conduct an empirical study using security-related news reviews. The experimental results demonstrate the effectiveness of our proposed approach.

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, Douban.com. The experimental results show the effectiveness of our approach.

Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence the behavior in the users' best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous pruning method was proposed. However, inconsistency of the measure for rule pruning may hinder its performance. To overcome this problem, we develop a new pruning method to achieve rule pruning in actionable rule discovery. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results based on a benchmark terrorism dataset indicate that our approach outperforms those from previous research.

Discovering temporal patterns and changes in tobacco use has important practical implications in tobacco control. This paper presents one of the first comprehensive international studies of seasonal smoking patterns based on online searches performed. Using periodogram and cross-correlation, we find that smoking-related search behavior shows strong seasonality effect across countries. In addition, there are significant pairwise associations between such seasonality in different countries.

Spatio-temporal data mining is finding applications in many domains, such as public health, public safety, financial fraud detection, transportation, and product lifecycle management. Correlation analysis is an important spatio-temporal mining technique for unveiling spatial and temporal relationships among multiple event types. This paper presents a new measure for assessing and analyzing spatio-temporal cross-correlations. This measure extends Ripley’s a widely used measure of spatial correlation, with an additional temporal dimension. Empirical studies using real-world data show that the new measure can lead to a more discriminating and flexible spatio-temporal data analysis framework. In contrast with its predecessor, this measure also allows the discovery of leading (and potentially causal) event types whose occurrences precede those of other event types. Findings from analyses employing this measure may bear important managerial implications.

In order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in the real world, we apply the Artificial societies, Computational experiments, and Parallel execution (ACP) approach to public health emergency management. We use the largest collective outbreak of H1N1 influenza at a Chinese university in 2009 as a case study. We build an artificial society to simulate the outbreak at the university. In computational experiments, aiming to obtain comparable results with the real data, we apply the same intervention strategy as that was used during the real outbreak. Then, we compare experiment results with real data to verify our models, including spatial models, population distribution, weighted social networks, contact patterns, students' behaviors, and models of H1N1 influenza disease, in the artificial society. In the phase of parallel execution, alternative intervention strategies are proposed to control the outbreak of H1N1 influenza more effectively. Our models and their application to intervention strategy improvement show that the ACP approach is useful for public health emergency management.

Individual credibility assessment is an ever-growing requirement in different applications including border crossing, airport checkpoint screening, criminal investigations, etc. In this paper, we describe a heterogeneous sensor fusion algorithm based on the AdaBoost algorithm and a speech emotional recognition technique in order to develop an automatic noninvasive credibility assessment system. Our initial results based on two available sensors show a promising increase in the accuracy and performance of the system.

Actions are the primary way an entity interacts with other entities and acts on the external world. Action knowledge is of vital importance for behavior modeling, analysis and prediction in security informatics. In this paper, we present our approach to action knowledge extraction from Web textual data. Our approach is based on mutual bootstrapping with knowledge reasoning, which can acquire more action knowledge types and require less human participation compared with the related work. We evaluate the performance of our method and demonstrate its effectiveness through experiment.

Border safety is a critical part of national and international security. The Department of Homeland Security (DHS) searches vehicles entering the country at land borders for drugs and other contraband. However, this process is time-consuming and operational efficiency is needed for smooth operations at the border. To aid in the screening of vehicles, we propose to examine traffic patterns at checkpoints using burst detection algorithms. Our results show that the overall traffic at the border shows bursting patterns attributable to week days and the holiday seasons. In addition, using local law-enforcement data we also find that traffic with prior contacts with law-enforcement shows a bursting pattern distinct from other traffic. We also find that such bursts in suspicious traffic can be attributable to increases in vehicular traffic associated with certain kinds of criminal activity. This information can be used to specifically target vehicles searches during primary screening at ports and in the surrounding areas.

Opinion mining has gained increasing attention and shown great practical value in recent years. Existing research on opinion mining mainly focuses on the extraction of lexicon orientation and opinion targets. The explanations of opinions, which are potentially valuable for many applications, are totally ignored. To address this specific research challenge, in this paper, we propose an approach to extract the explanation of reason and/or consequence behind an opinion via learning word pairs and using causal indicators from Chinese online reviews. We also improve our word pair based method by constructing clusters of word paris. Experiments on a Chinese business review corpus show that our method is feasible and effective.

Click fraud (CF) has become a serious problem in the online advertising, making the anti-CF issue quite important. In this paper, we analyze the effects of the price determination model on the CF situations in online advertising. Our theoretical results show that the flat-rate model can induce more click frauds than the real-time-bidding model. Our finding is validated with a real advertising dataset.

Food safety events are typical public security events that draw great public concern. In food safety events, millions of netizens pay close attention to the event, express their opinions online and thus influence the decisions of government or food producers. Modeling netizen groups, especially the dynamics of their opinions in these events, can help us understand the mechanism and evolvement of such events and provide valuable insights for social management. However, conventional computational models, such as agent-based models, are usually constructed manually. In this paper, we propose an approach to acquiring netizen group's opinions from online comments to facilitate the modeling of food safety events. We conduct experimental study on typical events happened in China and empirically evaluate the performance of our proposed approach. The results verify the effectiveness of the approach.

Despite the increased prevalence of sentiment-related information on the Web, there has been limited work on focused crawlers capable of effectively collecting not only topic-relevant but also sentiment-relevant content. In this article, we propose a novel focused crawler that incorporates topic and sentiment information as well as a graph-based tunneling mechanism for enhanced collection of opinion-rich Web content regarding a particular topic. The graph-based sentiment (GBS) crawler uses a text classifier that employs both topic and sentiment categorization modules to assess the relevance of candidate pages. This information is also used to label nodes in web graphs that are employed by the tunneling mechanism to improve collection recall. Experimental results on two test beds revealed that GBS was able to provide better precision and recall than seven comparison crawlers. Moreover, GBS was able to collect a large proportion of the relevant content after traversing far fewer pages than comparison methods. GBS outperformed comparison methods on various categories of Web pages in the test beds, including collection of blogs, Web forums, and social networking Web site content. Further analysis revealed that both the sentiment classification module and graph-based tunneling mechanism played an integral role in the overall effectiveness of the GBS crawler.

Pay-per-click (PPC) advertising is being seriously threatened by the click fraud (CF). In this paper, we report a game-theoretic analysis of the incentives and consequences of CF involving Web content publishers. We find that the publisher competition induces CF, harming the efficiency especially the publisher payoff allocation in the market.

Forecasting group behavior is critical to national and international security. Various forecasting methods have been developed previously. However, the majority of them are data-driven methods and rely heavily on the structured data which are often hard to obtain. To overcome the limitation of previous methods, we propose a novel plan recognition method for detecting multiple group behavior based on graph search. We further conduct human experiments in security informatics domain to empirically evaluate our proposed method. The experimental results show the effectiveness of our method.

Many security-related applications can benefit from constructing models to predict the behavior of an entity. However, such models do not provide the user with explicit knowledge that can be directly used to influence the behavior for his/her interest. This type of knowledge is called actionable knowledge. Actionability is a very important aspect of the interestingness of mined patterns. In this paper, we formally define a new problem of mining actionable behavioral rules from group data. We also propose an algorithm for solving the problem. Using terrorism group data, our experiment shows the validity of our approach as well as the practical value of our defined problem in security informatics.

Listwise approach is an important method to solve practical Web search problem in learning to rank. In this paper, we first analyze the practical Web search problem and construct the model to solve it. Then we propose an algorithm called DiffRank which can apply boosting technology to learning to rank in listwise. Through theoretical analysis, we prove that the upper bound of training error can be reduced in our proposed algorithm. The experimental results further verify our theoretical analysis and demonstrate that our approach can better perform in practical Web search than other state-of-the-art listwise algorithms.

A large number of collaborative filtering algorithms have been proposed in the literature as the foundation of automated recommender systems. However, the underlying justification for these algorithms is lacking, and their relative performances are typically domain and data dependent. In this paper, we aim to develop initial understanding of the recommendation model/algorithm validation and selection issues based on the graph topological modeling methodology. By representing the input data in the form of consumer–product interactions as a bipartite graph, the consumer–product graph, we develop bipartite graph topological measures to capture patterns that exist in the input data relevant to the transaction-based recommendation task. We observe the deviations of these topological measures of real-world consumer–product graphs from the expected values for simulated random bipartite graphs. These deviations help explain why certain collaborative filtering algorithms work for particular recommendation data sets. They can also serve as the basis for a comprehensive model selection framework that “recommends” appropriate collaborative filtering algorithms given characteristics of the data set under study. We validate our approach using three real-world recommendation data sets and demonstrate the effectiveness of the proposed bipartite graph topological measures in selection and validation of commonly used heuristic-based recommendation algorithms, the user-based, item-based, and graph-based algorithms.

Community Question Answering (CQA) is becoming a popular Web 2.0 application. By analyzing evolutionary topic patterns from CQA applications, one can gain insights into user interests and user responses to external events. This paper proposes a novel evolutionary topic pattern mining approach. This approach consists of three components: 1) extraction of the topics being discussed through a temporal analysis; 2) discovery of topic evolutions and construction of evolutionary graphs of extracted topics; and 3) life cycle modeling of the extracted topics. We show empirically the effectiveness of our approach using two real-world data sets.

Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches.

Terrorism organizations are devising increasingly sophisticated plans to conduct attacks. The ability of emulating or constructing attack plans by potential terrorists can help us understand the intents and motivation behind terrorism activities. A feasible computational method to construct plans is planning technique in AI. Traditionally, AI planning methods rely on a predefined domain theory which is compiled by domain experts manually. To facilitate domain theory construction and plan generation, we propose a method to construct domain theory automatically from free text data. The effectiveness of our proposed approach is evaluated empirically through experimental studies using real world terrorist plans .

The explosive growth of the user-generated content on the Web has offered a rich data source for mining opinions. However, the large number of diverse review sources challenges the individual users and organizations on how to use the opinion information effectively. Therefore, automated opinion mining and summarization techniques have become increasingly important. Different from previous approaches that have mostly treated product feature and opinion extraction as two independent tasks, we merge them together in a unified process by using probabilistic models. Specifically, we treat the problem of product feature and opinion extraction as a sequence labeling task and adopt Conditional Random Fields models to accomplish it. As part of our work, we develop a computational approach to construct domain specific sentiment lexicon by combining semi-structured reviews with general sentiment lexicon, which helps to identify the sentiment orientations of opinions. Experimental results on two real world datasets show that the proposed method is effective.

Abstract With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine‐grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature‐opinion tuples are generated. Experimental results on real‐world datasets show that the proposed approach is effective.

This paper investigates the collaboration patterns and research topic trends in the publications of the IEEE Transactions on Intelligent Transportation Systems (T-ITS) over the past decade. We find that coauthorship is prevalent and that the coauthorship networks possess the scale-free property on high degree nodes. Collaborations usually occur within the same research institutions and countries. Interorganization/region collaboration structures are usually connected through a few productive/high-impact authors. Typical international collaborations are between the U.S. and other countries such as China, Germany, U.K., and Italy. Active topics studied in IEEE T-ITS publications in the past ten years include traffic management and machine vision, among others. Authors can be partitioned into common interest groups, of which machine vision and automatic vehicle control attract more researchers.

This paper presents a bibliographic analysis of the papers published in the IEEE Transactions on Intelligent Transportation Systems (T-ITS). We identify the most productive and high-impact authors, institutions, and countries/regions. We find that research on intelligent transportation systems is dominated by U.S. researchers and institutions and that China and Japan are the second most productive countries. According to this analysis, M. M. Trivedi, N. P. Papanikolopoulos, and P. A. Ioannou are the three most productive and influential authors in the IEEE T-ITS, whereas the Massachusetts Institute of Technology, Cambridge, the University of California, San Diego, and the University of Minnesota, Minneapolis, are three of the most productive and influential institutions in the IEEE T-ITS.

Accurate and timely detection of infectious disease outbreaks provides valuable information which can enable public health officials to respond to major public health threats in a timely fashion. However, disease outbreaks are often not directly observable. For surveillance systems used to detect outbreaks, noises caused by routine behavioral patterns and by special events can further complicate the detection task. Most existing detection methods combine a time series filtering procedure followed by a statistical surveillance method. The performance of this "two-step” detection method is hampered by the unrealistic assumption that the training data are outbreak-free. Moreover, existing approaches are sensitive to extreme values, which are common in real-world data sets. We considered the problem of identifying outbreak patterns in a syndrome count time series using Markov switching models. The disease outbreak states are modeled as hidden state variables which control the observed time series. A jump component is introduced to absorb sporadic extreme values that may otherwise weaken the ability to detect slow-moving disease outbreaks. Our approach outperformed several state-of-the-art detection methods in terms of detection sensitivity using both simulated and real-world data.

Modeling and detecting bursts in data streams is an important area of research with a wide range of applications. In this paper, we present a novel method to analyze and identify correlated burst patterns by considering multiple data streams that coevolve over time. The main technical contribution of our research is the use of a dynamic probabilistic network to model the dependency structures observed within these data streams. Such dependencies provide meaningful information concerning the overall system dynamics and should be explicitly integrated into the burst detection process. Using both synthetic scenarios and two real-world datasets, we compare our method with an existing burst-detection algorithm. Initial experimental results indicate that our approach allows for more balanced and accurate burst quantification.

Cultural modeling is an emergent and promising research area in social computing. It aims to develop behavioral models of groups and analyze the impact of culture factors on group behavior using computational methods. Classification methods play a critical role in cultural modeling domain. As various cultural-related datasets possess different properties, for group behavior prediction, it is important to gain a computational understanding of the performance of various classification methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling dataset and analyze the experimental results.

Collaborative tagging sites allow users to save and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering. This paper proposes a probabilistic approach to leverage information embedded in tags to improve the effectiveness of Web page recommendation in a social information management context. In our approach, the probability of a Web page visit by a user is estimated by summing up the relevance of this Web page to this user's tags, and then those pages with the highest probabilities are recommended. Experiments using two real-world collaborative tagging datasets show that our algorithms outperform the common collaborative filtering methods.

Cultural modeling is an emergent and promising research area in social computing. It aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In cultural modeling, it is expected that classifiers yield good performance. However, the performance of standard classifiers is often severely hindered in practice due to the imbalanced distribution of class in cultural data. In this paper, we identify class imbalance problem in cultural modeling domain. To handle the problem, we propose a user involved solution employing the receiver operating characteristic (ROC) analysis for classification algorithms with sampling approaches. Finally, we conduct experiment to verify the effectiveness of the proposed solution.

A large portion of online news articles and postings are not originally created but reprinted or re-posted from other online news sources or portals. In this paper, we analyze the dynamics of online news propagation, using a large collection of Chinese online news activity data. We characterize prominent features of online news diffusion and compare them against the spreading patterns of the epidemic. Several critical factors influencing the news propagation process are identified, including the centrality and selectivity of source portals, and event variability.

This paper is motivated to utilize results from opinion mining to facilitate social network analysis. We introduce the concept of Opinion Networks and propose a PageRank-like algorithm, named OpinionRank, to rank the nodes in an opinion network. This proposed approach has been applied to real-world datasets and initial experiments indicate that the sentiment information is helpful for finding leaders of online communities and that the OpinionRank method outperforms benchmark methods that ignore sentiment information.

Abstract User‐generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule‐based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning‐based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning‐based approaches.

The threat of infectious disease outbreaks and bioterrorism attacks has stimulated the development of syndromic surveillance systems, which focus on using pre-diagnostic data such as emergency department chief complaints and over-the-counter (OTC) drug sales to detect bioterrorism events in a timely manner. A key function of syndromic surveillance systems is detecting possible bioterrorism events from time series data. In this paper, we propose a novel temporal outbreak detection method based on the Markov switching model, a special case of hidden Markov models. The model is motivated to address several computational problems with existing detection schemes concerning the inconsistency in parameter estimation and the resulting undesired detection performance. Preliminary evaluation using simulated outbreaks injected on authentic time series shows that our method outperforms benchmark methods in terms of outbreak detection speed and detection sensitivity at given levels of false alarm rates.

The 12 papers in this special section focus on intelligence and security informatics. They are summarized here.

Infectious disease informatics, as a sub-field of security informatics, is concerned with development of the science and technologies needed for collecting, sharing, reporting, analyzing, and visualizing infectious disease data; and for providing data and decision-making support for infectious disease prevention, detection, and management. Syndromic surveillance is a major study area of infectious disease informatics, focusing on identifying in a timely manner possible infectious disease outbreaks based on pre-diagnostic data. Free-text chief complaints (CCs), short phrases describing reasons for patients' emergency department visits, are a major source of data for syndromic surveillance. For surveillance purposes, CCs need to be classified into syndromic categories. However, the lack of standard vocabulary and high-quality encoding of CCs hinder effective classification. To meet this challenge, we have developed an ontology-enhanced automatic CC classification approach. Exploiting semantic relations in the UMLS, a medical ontology, this approach is motivated to address the CC word variation problem in general and to meet the specific need for a flexible classification approach capable of handling multiple sets of syndrome categories.

Motivated by the importance of infectious disease informatics (IDI) and the challenges to IDI system development and data sharing, we design and implement BioPortal, a Web-based IDI system that integrates cross-jurisdictional data to support information sharing, analysis, and visualization in public health. In this paper, we discuss general challenges in IDI, describe BioPortal's architecture and functionalities, and highlight encouraging evaluation results obtained from a controlled experiment that focused on analysis accuracy, task performance efficiency, user information satisfaction, system usability, usefulness, and ease of use.

In many security informatics applications, it is important to monitor traffic over various communication channels and efficiently identify those communications that are unusual for further investigation. This paper studies such anomaly detection problems using a graph-theoretic link prediction approach. Data from the publicly-available Enron email corpus were used to validate the proposed approach.

This paper presents a novel ontology-based approach to classify free-text chief complaints (CCs) into syndrome categories. This approach exploits the semantic relations in a medical ontology to address the CC word variation problem. Initial computational experiments indicate that this ontology-based approach is able to improve significantly the probability that a CC can be correctly classified as a syndrome.

Workflow systems provide the key technology to enable business-process automation. One important function of workflow management is role resolution, i.e., the mechanism of assigning tasks to individual workers at runtime according to the role qualification defined in the workflow model. Role-resolution decisions directly affect the productivity of workers in an organization, and consequently affect corporate profitability. Therefore it is important to develop effective policies governing these decisions. However, there has not been a formal treatment of role-resolution policies in the literature. In this paper, we analyze role-resolution policies used in current workflow practice and propose new optimization-based policies that utilize online batching. Through a computational study, we examine three workflow-performance measures including maximum flowtime, average workload, and workload variation under these policies in different business scenarios. These scenarios vary by overall system load, task-processing-time distribution, and the number of workers. Based on computational results, we obtain the following insights that can help guide the selection of role-resolution policies. (a) As the overall system load increases, the benefit of using batching-based online optimization policies becomes more significant. (b) Processing-time variation has a major impact on workflow performance, and higher variation favors optimization-based policies. (c) Online optimization has the potential to reduce average workload significantly, and to reduce workload variation significantly as well.

Intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applications, through an integrated technological, organizational, and policy-based approach. This paper summarizes the broad application and policy context for this emerging field. Three detailed case studies are presented to illustrate several key ISI research areas, including cross-jurisdiction information sharing; terrorism information collection, analysis, and visualization; and "smart-border" and bioterrorism applications. A specific emphasis of this paper is to note various homeland-security-related applications that have direct relevance to transportation researchers and to advocate security informatics studies that tightly integrate transportation research and information technologies.

Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance.

Web caching technology has been widely used to improve the performance of the Web infrastructure and reduce user-perceived network latencies. Proxy caching is a major Web caching technique that attempts to serve user Web requests from one or a network of proxies located between the end user and Web servers hosting the original copies of the requested objects. This paper surveys the main technical aspects of proxy caching and discusses recent developments in proxy caching research including caching the "uncacheable" and multimedia streaming objects, and various adaptive and integrated caching approaches.

No abstract available.

In this paper, we study the architectures and routing techniques used in cooperative web caching and the techniques to evaluate the performances of caching systems. In the first part, the paper studies those advantages and disadvantages of different architectures of caching systems. In the second part, we describe routing techniques combined with these architectures of caching systems. And in the rest of this paper, we discuss the performance parameters and tools with which people can evaluate the performance of different web caching mechanisms.

The cache coherence and prefetching are both important mechanisms of web caching. The coherence is for updating the stale documents, and prefetching is for reducing the response latency. Based on the analysis of major algorithms of coherence and prefetching, we have identified the difference and commonness of the two mechanisms. Both mechanisms need the collaboration of proxy client and web server. Therefore, if coherence and prefetching are combined, the number of requests the web server receives will decrease, as well as the corresponding connecting time (e.g. DNS lookup, TCP connection, HTTP head request). In addition, this combination will save the network bandwidth.

This paper studied the state-of-art in web caching schemes and techniques. An introduction on web caching was presented at the first part. The second part focused on the web caching schemes and architectures. Some fundamental issues on web caching systems were given in the third part. Finally, we discussed some research and industrial frontier in web caching system.

Abstract It has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta‐search and document categorization. Meta‐search engines improve precision by selecting and integrating search results from generic or domain‐specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta‐search engine that has real‐time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing MetaSpider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.

We are investigating techniques for developing distributed and adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system architecture has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We have implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making, investment counseling, health care and electronic commerce.

Practical optimization problems such as job-shop scheduling often involve optimization criteria that change over time. Repair-based frameworks have been identified as flexible computational paradigms for difficult combinatorial optimization problems. Since the control problem of repair-based optimization is severe, reinforcement learning (RL) techniques can be potentially helpful. However, some of the fundamental assumptions made by traditional RL algorithms are not valid for repair-based optimization. Case-based reasoning compensates for some of the limitations of traditional RL approaches. We present a case-based reasoning RL approach, implemented in the C/sub A/B/sub I/NS system, for repair-based optimization. We chose job-shop scheduling as the testbed for our approach. Our experimental results show that C/sub A/B/sub I/NS is able to effectively solve problems with changing optimization criteria which are not known to the system and only exist implicitly in a extensional manner in the case base.