Audio platforms have evolved beyond entertainment.They have become central to public discourse, from podcasts and radio to What-sApp voice notes and live streams.With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation.Yet existing factchecking pipelines are mostly designed for written claims, overlooking the unique properties of spoken media.We argue that audio misinformation is not merely textual content with transcripts: it is structurally different because it is both spoken-carrying persuasive force through prosody, pacing, and emotion-and conversational-unfolding across turns, speakers, and episodes.These dual properties introduce verification difficulties that traditional methods rarely face.This position paper synthesizes evidence across modalities and platforms, examines datasets and methods, and highlights why existing pipelines fail on audio.We argue that advancing fact-checking requires rethinking verification pipelines around the spoken and conversational realities of audio.

Jason S Lucas, Matt Murtagh White, Ali Al-Lawati, Uchendu Uchendu, Adaku Uchendu, Dongwon Lee. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning.While prior work shows that GraphRAG responses may leak retrieved subgraphs, the feasibility of queryefficient reconstruction of the hidden graph structure remains unexplored under realistic query budgets.We study a budget-constrained black-box setting where an adversary adaptively queries the system to steal its latent entity-relation graph.We propose Agentic Graph Extraction Attack (AGEA), a framework that leverages a novelty-guided exploration-exploitation strategy, external graph memory modules, and a two-stage graph extraction pipeline combining lightweight discovery with LLM-based filtering.We evaluate AGEA on medical, agriculture, and literary datasets across Microsoft-GraphRAG and LightRAG systems.Under identical query budgets, AGEA significantly outperforms prior attack baselines, recovering up to 90% of entities and relationships while maintaining high precision.These results demonstrate that modern GraphRAG systems are highly vulnerable to structured, agentic extraction attacks, even under strict query limits.The code is available at https://github.com/shuashua0608/AGEA.

Multimodal Large Language Models (MLLMs) are renowned for their superior instructionfollowing and reasoning capabilities across diverse problem domains.However, existing benchmarks primarily focus on assessing factual and logical correctness in downstream tasks, with limited emphasis on evaluating MLLMs' ability to interpret pragmatic cues and intermodal relationships.To address this gap, we assess the competency of MLLMs in performing Multimodal Discourse Analysis (MDA) using Coherence Relations.Our benchmark, CORDIAL, encompasses a broad spectrum of Coherence Relations across 3 different discourse domains at varying levels of granularity.Through our experiments on 10+ MLLMs employing different prompting strategies, we show that even top models like Gemini 1.5 Pro and GPT-4o fail to match the performance of simple classifier-based baselines.This study emphasizes the need to move beyond similaritybased metrics and adopt a discourse-driven framework for evaluating MLLMs, providing a more nuanced assessment of their capabilities.The benchmark and code are available at: ht tps://aashish2000.github.

The rapid advancement of Large Language Models (LLMs) has revolutionized text generation but also raised concerns about potential misuse, making detecting LLM-generated text (AI text) increasingly essential.While prior work has focused on identifying AI text and effectively checkmating it, our study investigates a less-explored territory: portraying the nuanced distinctions between human and AI texts across text segments (introduction, body, and conclusion).Whether LLMs excel or falter in incorporating linguistic ingenuity across text segments, the results will critically inform their viability and boundaries as effective creative assistants to humans.Through an analogy with the structure of chess games, comprising opening, middle, and end games, we analyze segment-specific patterns to reveal where the most striking differences lie.Although AI texts closely resemble human writing in the body segment due to its length, deeper analysis shows a higher divergence in features dependent on the continuous flow of language, making it the most informative segment for detection.Additionally, human texts exhibit greater stylistic variation across segments, offering a new lens for distinguishing them from AI. Overall, our findings provide fresh insights into human-AI text differences and pave the way for more effective and interpretable detection strategies.Codes available at https://github.com/tripto03/chess inspired human ai text distinction.

The rapid advancement of Large Language Models (LLMs) has enabled the generation of highly realistic synthetic data.We identify a new vulnerability, LLMs generating convincing career trajectories in fake resumes and explore effective detection methods.To address this challenge, we construct a dataset of machinegenerated career trajectories using LLMs and various methods, and demonstrate that conventional text-based detectors perform poorly on structured career data.We propose Career-Scape, a novel heterogeneous, hierarchical multi-layer graph framework that models career entities and their relations in a unified global graph built from genuine resumes.Unlike conventional classifiers that treat each instance independently, CareerScape employs a structure-aware framework that augments user-specific subgraphs with trusted neighborhood information from a global graph, enabling the model to capture both global structural patterns and local inconsistencies indicative of synthetic career paths.Experimental results show that CareerScape outperforms state-of-the-art baselines by 5.8-85.0%relatively, highlighting the importance of structureaware detection for machine-generated content.Our codebase is available at https://github. com/mickeymst/careerscape.

Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing serious privacy risks.To mitigate this, MLLM unlearning methods are proposed, which finetune MLLMs to forget sensitive information.However, it remains unclear whether the knowledge has been truly forgotten or just hidden in the model.Therefore, we propose to study a novel problem of MLLM unlearning attack, which aims to recover the unlearned knowledge of an unlearned MLLM.To achieve the goal, we propose a novel framework-Stealthy Unlearning Attack (SUA)-that learns a universal noise pattern.When applied to input images, this noise can trigger the model to reveal unlearned content.While pixel-level perturbations may be visually subtle, they can be detected in the semantic embedding space, making such attacks vulnerable to potential defenses.To improve stealthiness, we introduce an embedding alignment loss that minimizes the difference between the perturbed and denoised image embeddings, ensuring that the attack remains semantically unnoticeable.Experimental results show that SUA can effectively recover unlearned information from MLLMs.Furthermore, the learned noise generalizes well-i.e., a single perturbation trained on a few samples can reveal forgotten contents in unseen images.

Artificial intelligence (AI) synthesized faces—so called deepfake images—have been increasingly used for malicious intent and have resulted in prominently adverse impact. Because online users must contend with discerning fake from real, great emphasis has been placed on enhancing human detection of deepfake images. We conducted an online human-subject study (N=237), investigating the effect of three training strategies (explicit training with visible artifacts in synthetic faces, implicit training with experiencing the generation of synthetic faces using real human faces, and a combination of both artifact and generation) on participants’ detection of synthetic faces generated by the state-of-the-art StyleGAN techniques. Comparing participants’ deepfake detection across three phases (baseline in phase 1 without any training, phase 2 after one training session, and phase 3 after the other training session), we found that all training strategies effectively enhanced participants’ detection of AI-synthesized faces and their decision confidence. We also explored factors that impact participants’ learning and decision-making of deepfake detection. Responses to the open-ended question revealed that participants developed generalized strategies and utilized artifacts beyond the training. Our quantitative and qualitative results provide nuanced insights into the promises and limitations of the training strategies. In addition to advancing theoretical understanding of human training in the context of deepfake image detection, our study findings hold practical implications for interface design.

Political misinformation, particularly harmful when it aligns with individuals' preexisting beliefs and political ideologies, has become widespread on social media platforms. In response, platforms like Facebook and X introduced warning messages leveraging fact-checking results from third-party fact-checkers to alert users against false content. However, concerns persist about the effectiveness of these fact-checks, especially when fact-checkers are perceived as politically biased. To address these concerns, this study presents findings from an online human-subject experiment (N=216) investigating how the political stances of fact-checkers influence their effectiveness in correcting misbeliefs about political misinformation. Our findings demonstrate that partisan fact-checkers can decrease the perceived accuracy of political misinformation and correct misbeliefs without triggering backfire effects. This correction is even more pronounced when the misinformation aligns with individuals' political ideologies. Notably, while previous research suggests that fact-checking warnings are less effective for conservatives than liberals, our results suggest that explicitly labeled partisan fact-checkers, positioned as political counterparts to conservatives, are particularly effective in reducing conservatives' misbeliefs toward pro-liberal misinformation.

Online texts with toxic content are a clear threat to the users on social media in particular and society in general. Although many platforms have adopted various measures (e.g., machine learning-based hate-speech detection systems) to diminish their effect, toxic content writers have also attempted to evade such measures by using cleverly modified toxic words, so-called human-written text perturbations. Therefore, to help build automatic detection tools to recognize those perturbations, prior methods have developed sophisticated techniques to generate diverse adversarial samples. However, we note that these ``algorithm"-generated perturbations do not necessarily capture all the traits of ``human"-written perturbations. Therefore, in this paper, we introduce a novel, high-quality dataset of human-written perturbations, named as NoisyHate, that was created from real-life perturbations that are both written and verified by human-in-the-loop. We show that perturbations in NoisyHate have different characteristics than prior algorithm-generated toxic datasets show and thus can be particularly useful to help develop better toxic speech detection solutions. We also provide basic benchmark on the potential utilities of NoisyHate in perturbation normalization and understanding tasks. Both dataset and source code are publicly available.

The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions--i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively. The codebase of CAPER is available at https://github.com/Bigdasgit/CAPER.

Jooyoung Lee, Toshini Agrawal, Adaku Uchendu, Thai Le, Jinghui Chen, Dongwon Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Delvin Ce Zhang, Dongwon Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The eviction of tenants is a pressing problem, which is prevalent among low-income renters in the USA, and has devastating consequences. Despite the presence of various measures to combat evictions, identifying high-need regions and tenant groups is highly challenging in many regions due to a lack of access to eviction records (partly because of some infrastructural/policy constraints). In response to this information gap, this paper proposes a solution driven by Machine Learning (ML) to monitor eviction status at various spatial resolutions using Airbnb data when ground-truth eviction data is inaccessible. In particular, we begin by demonstrating the potential of utilizing Airbnb data to build ML-driven methods for distinguishing different neighborhoods across different spatial resolutions with respect to eviction status. We then proceed to develop an ML model capable of learning eviction status levels from Airbnb data, even in the absence of ground-truth labels. Empirical evidence is presented, showcasing the model's performance on par with several robust fully-supervised ML models that had access to ground-truth labels during training. Finally, we conduct a set of cross-region tests to comprehensively study the generalizability of the achieved performance across various unseen regions in the USA that were not used during model training. The code of this project can be accessed via https://github.com/maryam-tabar/Airbnb-Eviction.

Authorship Attribution (AA) and Authorship Obfuscation (AO) are two competing tasks of increasing importance in privacy research. Modern AA leverages an author's consistent writing style to match a text to its author using an AA classifier. AO is the corresponding adversarial task, aiming to modify a text in such a way that its semantics are preserved, yet an AA model cannot correctly infer its authorship. To address privacy concerns raised by state-of-the-art (SOTA) AA methods, new AO methods have been proposed but remain largely impractical to use due to their prohibitively slow training and obfuscation speed, often taking hours. To this challenge, we propose a practical AO method, ALISON, that (1) dramatically reduces training/obfuscation time, demonstrating more than 10x faster obfuscation than SOTA AO methods, (2) achieves better obfuscation success through attacking three transformer-based AA methods on two benchmark datasets, typically performing 15% better than competing methods, (3) does not require direct signals from a target AA classifier during obfuscation, and (4) utilizes unique stylometric features, allowing sound model interpretation for explainable obfuscation. We also demonstrate that ALISON can effectively prevent four SOTA AA methods from accurately determining the authorship of ChatGPT-generated texts, all while minimally changing the original text semantics. To ensure the reproducibility of our findings, our code and data are available at: https://github.com/EricX003/ALISON.

Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Recent studies have shown that a small number of buildings account for a significant portion of evictions in major U.S. cities, suggesting targeted policy interventions for these hotspots. However, focusing solely on eviction volumes can mislead policymakers by implying that property owners are the primary drivers of high eviction rates. This study investigates the spatial structure of eviction filings at the Census Block Group (CBG) level to determine if high eviction rates are due to neighborhood characteristics or other factors like landlords’ practices. We addressed three research questions: 1) the relationship between eviction filings due to nonpayment of rent and neighborhood characteristics, 2) the differences between eviction filings due to nonpayment and those for other reasons, and 3) the extent to which high rates of eviction filings in certain CBGs can be attributed to neighborhood characteristics versus unexplained spatial effects. We used Restricted Spatial Generalized Linear Mixed Models (RSGLMMs) with Hamiltonian Monte Carlo (HMC) sampling to estimate neighborhood fixed effects and spatial random effects, using data from Dallas County. Our findings confirm that important neighborhood factors identified in previous studies are consistently significant. Our spatial analysis revealed a noticeable difference between raw eviction filing counts and those adjusted for neighborhood characteristics, identifying CBGs with excessive eviction filings even after accounting for the neighborhood context. Based on these results, we propose a dual-track policy intervention: for hotspot buildings in CBGs with moderate spatial effects, we recommend tenant support measures like rental assistance and legal aid; for those with high spatial effects, we suggest prioritizing in-depth investigations of these buildings and landlord-focused interventions such as education on fair housing laws and landlord-tenant mediation services. All relevant code and data from this project are available in the GitHub repository: https://github.com/yilmajung/eviction2024repo.

Despite substantial advancements in various fields of AI, computational research in career and job domains has been significantly hindered by a critical lack of accessible datasets. This limitation is mainly due to the proprietary nature of job platforms, which restrict the sharing of job-domain datasets with the research community. The scarcity is particularly pronounced for career trajectory and resume datasets, severely constraining academic researchers in developing and evaluating new models. In this paper, we address the crucial issue of resume dataset unavailability in the job domain, identified through our comprehensive comparison of existing job-domain machine learning studies. To the best of our knowledge, we introduce OpenResume, the first publicly available, anonymized, and structured resume dataset, specifically designed for job-domain downstream tasks. This dataset aims to catalyze advancements in AI and foster new markets for machine learning and data science within career trajectory modeling. OpenResume is comprehensively processed from real-world resume data. We anonymize and substitute personal identifiers and company names, normalize job titles into ESCO-based ones (i.e., one of the most common occupation taxonomies), and employ differential privacy techniques on temporal features to ensure open accessibility and privacy protection. Additionally, we augment OpenResume with a synthetically generated resume dataset derived from the post-processed real-world data, extending its diversity and utility. To demonstrate that OpenResume retains challenges and properties similar to real-world job datasets, we benchmark OpenResume on state-of-the-art job-domain prediction models across four prevalent downstream tasks: (1) next job title prediction, (2) next company prediction, (3) turnover prediction, and (4) link prediction. Our experimental results show that these job-domain models perform comparably on OpenResume and the original data across all tasks, demonstrating OpenResume as a valuable career trajectory dataset for both academic research and practical applications. We also indicate the OpenResume applicability for the other eight downstream tasks. Our datasets are available at: https://tinyurl.com/OpenResumeData.

Recent advances in Large Language Models (LLMs) have enabled the generation of open-ended high-quality texts, that are non-trivial to distinguish from human-written texts. We refer to such LLM-generated texts as deepfake texts. There are currently over 72K text generation models in the huggingface model repo. As such, users with malicious intent can easily use these open-sourced LLMs to generate harmful texts and dis/misinformation at scale. To mitigate this problem, a computational method to determine if a given text is a deepfake text or not is desired–i.e., Turing Test (TT). In particular, in this work, we investigate the more general version of the problem, known as Authorship Attribution (AA), in a multi-class setting–i.e., not only determining if a given text is a deepfake text or not but also being able to pinpoint which LLM is the author. We propose TopFormer to improve existing AA solutions by capturing more linguistic patterns in deepfake texts by including a Topological Data Analysis (TDA) layer in the Transformer-based model. We show the benefits of having a TDA layer when dealing with imbalanced, and multi-style datasets, by extracting TDA features from the reshaped pooled_output of our backbone as input. This Transformer-based model captures contextual representations (i.e., semantic and syntactic linguistic features), while TDA captures the shape and structure of data (i.e., linguistic structures). Finally, TopFormer, outperforms all baselines in all 3 datasets, achieving up to 7% increase in Macro F1 score. Our code and datasets are available at: https://github.com/AdaUchendu/topformer

This study, focusing on two Reddit subcommunities of r/ToastMe and r/RoastMe, aims to (1) characterize and understand users (named Jekyll and Hyde) who simultaneously participate in two subreddits with opposing tones and purposes, (2) build predictive models detecting those Jekyll and Hyde users to assess how unique and idiosyncratic their characteristics are, and (3) investigate their motivations of participation and potential interaction between the two contrasting activities through a survey and one-on-one interviews. Our results reveal that the Jekyll and Hyde users are generally more active and popular than ordinary users. Also, they use assimilated language customized to each community’s tone. Combining these findings with their motivations unveiled through the survey and interviews, we conclude that the Jekyll and Hyde users are digitally culture-savvy, who know how to utilize online community benefits and enjoy each community’s culture by assimilating themselves into the community and observing its rules. Moreover, the users’ duality observed in this process underscores the dynamic and multifaceted nature of online personas. These findings highlight the need for a nuanced approach to understanding online behaviors and provide insights for designing healthier online environments, emphasizing the importance of clear community norms and the potential interplay of users’ activities across different communities.

To mitigate misinformation on social media, platforms such as Facebook have offered warnings to users based on the detection results of AI systems. With the evolution of AI detection systems, efforts have been devoted to applying explainable AI (XAI) to further increase the transparency of AI decision-making. Nevertheless, few factors have been considered to understand the effectiveness of a warning with AI explanations in helping humans detect misinformation. In this study, we report the results of three online human-subject experiments (N = 2,692) investigating the framing effect and the impact of an AI system’s reliability on the effectiveness of AI warning with explanations. Our findings show that the framing effect is effective for participants’ misinformation detection, whereas the AI system’s reliability is critical for humans’ misinformation detection and participants’ trust in the AI system. However, adding the explanations can potentially increase participants’ suspicions on miss errors (i.e., false negatives) in the AI system. Furthermore, more trust is shown in the AI warning without explanations condition. We conclude by discussing the implications of our findings.

From the observation that users reading news tend to not click outdated news, we propose the notion of 'lifetime' of news, with two hypotheses: (i) news has a shorter lifetime, compared to other types of items such as movies or e-commerce products; (ii) news only competes with other news whose lifetimes have not ended, and which has an overlapping lifetime (i.e., limited competitions). By further developing the characteristics of the lifetime of news, then we present a novel approach for news recommendation, namely, Lifetime-Aware News reCommEndeR System (LANCER) that carefully exploits the lifetime of news during training and recommendation. Using real-world news datasets (e.g., Adressa and MIND), we successfully demonstrate that state-of-the-art news recommendation models can get significantly benefited by integrating the notion of lifetime and LANCER, by up to about 40% increases in recommendation accuracy.

In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e.g., job recommendation, users’ career analysis, and turnover prediction). Job Title Normalization (JTN) is such a cleaning step to classify user-created non-standard job titles into normalized ones. However, solving the JTN problem is non-trivial with challenges: (1) semantic similarity of different job titles, (2) non-normalized user-created job titles, and (3) large-scale and long-tailed job titles in real-world applications. To this end, we propose a novel solution, named JAMES, that constructs three unique embeddings (i.e., graph, contextuat, and syntactic) of a target job title to effectively capture its various traits. We further propose a multi-aspect co-attention mechanism to attentively combine these embeddings, and employ neural logical reasoning representations to collaboratively estimate similarities between messy job titles and normalized job titles in a reasoning space. To evaluate JAMES, we conduct comprehensive experiments against ten competing models on a large-scale real-world dataset with over 350,000 job titles. Our experimental results show that JAMES significantly outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in NDCG@10, respectively. To further facilitate the acquisition of normalized job titles for job-domain applications, our JAMES API is available at: https://tinyurl.con JAMES-job-title-mapping.

Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (i.e., generating large-scale harmful and misleading content).To combat this emerging risk of LLMs, we propose a novel "Fighting Fire with Fire" (F3) strategy that harnesses modern LLMs' generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation.First, we leverage GPT-3.5-turbo to synthesize authentic and deceptive LLMgenerated content through paraphrase-based and perturbation-based prefix-style prompts, respectively.Second, we apply zero-shot incontext semantic reasoning techniques with cloze-style prompts to discern genuine from deceptive posts & news articles.In our extensive experiments, we observe GPT-3.5-turbo'szero-shot superiority for both in-distribution and out-of-distribution datasets, where GPT-3.5-turboconsistently achieved accuracy at 68-72%, unlike the decline observed in previous customized and fine-tuned disinformation detectors.Our codebase and dataset are available at https://github.com/mickeymst/F3.

Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

User-generated textual contents on the Internet are often noisy, erroneous, and not in correct grammar. In fact, some online users choose to express their opinions online through carefully perturbed texts, especially in controversial topics (e.g., politics, vaccine mandate) or abusive contexts (e.g., cyberbullying, hate-speech). However, to the best of our knowledge, there is no framework that explores these online "human-written" perturbations (as opposed to algorithm-generated perturbations). Therefore, we introduce an interactive system called CrypText. CrypText is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations. Specifically, CrypText helps look up, perturb, and normalize (i.e., de-perturb) texts. CrypText also provides an interactive interface to monitor and analyze text perturbations online. The demo is available at: https://lethaiq.github.io/anthro.

Associative inference is an adaptive, constructive process of memory that allows people to link related information to make novel connections. We conducted three online human-subjects experiments investigating participants’ susceptibility to associatively inferred misinformation and its interaction with their cognitive ability and how news articles were presented. In each experiment, participants completed recognition and perceived accuracy rating tasks for the snippets of news articles in a tweet format across two phases. At Phase 1, participants viewed real news only. At Phase 2, participants viewed both real and fake news. Critically, we varied whether the fake news at Phase 2 was inferred from (i.e., associative inference), associated with (i.e., association only), or irrelevant to (i.e., control) the corresponding real news pairs at Phase 1. Both recognition and perceived accuracy results showed that participants in the associative inference condition were more susceptible to fake news than those in the other conditions. Furthermore, hashtags embedded within the tweets made the obtained effects evident only for the participants of higher cognitive ability. Our findings reveal that associative inference can be a basis for individuals’ susceptibility to misinformation, especially for those of higher cognitive ability. We conclude by discussing the implications of our results for understanding and mitigating misinformation on social media platforms.

Following the 2016 US elections Twitter launched their Information Operations (IO) hub where they archive account activity connected to state linked information operations. In June 2020, Twitter took down and released a set of accounts linked to Turkey's ruling political party (AKP). We investigate these accounts in the aftermath of the takedown to explore whether AKP-linked operations are ongoing and to understand the strategies they use to remain resilient to disruption. We collect live accounts that appear to be part of the same network, ~30% of which have been suspended by Twitter since our collection. We create a BERT-based classifier that shows similarity between these two networks, develop a taxonomy to categorize these accounts, find direct sequel accounts between the Turkish takedown and the live accounts, and find evidence that Turkish IO actors deliberately construct their network to withstand large-scale shutdown by utilizing explicit and implicit signals of coordination. We compare our findings from the Turkish operation to Russian and Chinese IO on Twitter and find that Turkey's IO utilizes a unique group structure to remain resilient. Our work highlights the fundamental imbalance between IO actors quickly and easily creating free accounts and the social media platforms spending significant resources on detection and removal, and contributes novel findings about Turkish IO on Twitter.

Many online learning platforms and MOOCs incorporate some amount of video-based content into their platform, but there are few randomized controlled experiments that evaluate the effectiveness of the different methods of video integration. Given the large amount of publicly available educational videos, an investigation into this content's impact on students could help lead to more effective and accessible video integration within learning platforms. In this work, a new feature was added into an existing online learning platform that allowed students to request skill-related videos while completing their online middle-school mathematics assignments. A total of 18,535 students participated in two large-scale randomized controlled experiments related to providing students with publicly available educational videos. The first experiment investigated the effect of providing students with the opportunity to request these videos, and the second experiment investigated the effect of using a multi-armed bandit algorithm to recommend relevant videos. Additionally, this work investigated which features of the videos were significantly predictive of students' performance and which features could be used to personalize students' learning. Ultimately, students were mostly disinterested in the skill-related videos, preferring instead to use the platforms existing problem-specific support, and there was no statistically significant findings in either experiment. Additionally, while no video features were significantly predictive of students' performance, two video features had significant qualitative interactions with students' prior knowledge, which showed that different content creators were more effective for different groups of students. These findings can be used to inform the design of future video-based features within online learning platforms and the creation of different educational videos specifically targeting higher or lower knowledge students. The data and code used in this work can be found at https://osf.io/cxkzf/.

Past literature has illustrated that language models (LMs) often memorize parts of training instances and reproduce them in natural language generation (NLG) processes. However, it is unclear to what extent LMs “reuse” a training corpus. For instance, models can generate paraphrased sentences that are contextually similar to training samples. In this work, therefore, we study three types of plagiarism (i.e., verbatim, paraphrase, and idea) among GPT-2 generated texts, in comparison to its training data, and further analyze the plagiarism patterns of fine-tuned LMs with domain-specific corpora which are extensively used in practice. Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs’ plagiarism patterns vary based on their corpus similarity and homogeneity. Given that a majority of LMs’ training data is scraped from the Web without informing content owners, their reiteration of words, phrases, and even core ideas from training sets into generated texts has ethical implications. Their patterns are likely to exacerbate as both the size of LMs and their training data increase, raising concerns about indiscriminately pursuing larger models with larger training corpora. Plagiarized content can also contain individuals’ personal and sensitive information. These findings overall cast doubt on the practicality of current LMs in mission-critical writing tasks and urge more discussions around the observed phenomena. Data and source code are available at https://github.com/Brit7777/LM-plagiarism.

African smallholder farmers have struggled with low agricultural productivity for decades, partly due to their inability to proactively assess irrigation needs in their farms in the face of long-term climate change. In this paper, we tackle this challenge by employing data-driven techniques to develop forecasting tools for three widely used crop-productivity related variables (i.e., actual evapotranspiration, reference evapotranspiration, and net primary production), which can then be used by farmers to take corrective actions on their farms. Prior work in this domain, despite using data-driven methods, suffers from two major limitations: (i) they mainly focus on estimating variable values (as opposed to forecasting the future); and (ii) they mostly use classical Machine Learning (ML) prediction models, despite the abundance of data sufficient to train sophisticated deep learning models. To fill this research gap, we collaborate with PlantVillage, the world’s leading non-profit agricultural knowledge delivery platform for African farmers, to identify ∼2,200 smallholder farm locations, and gather remote-sensed data of these farms over a period of five years. Next, we propose CLIMATES, a meta-algorithm leveraging structural insights about temporal patterns of this time-series data to accurately forecast their future values. We conduct extensive experiments to evaluate its performance in this domain. Our experimental results show that CLIMATES outperforms several state-of-the-art time-series forecasting models. We also provide insights about the poor performance of some competing models. Our work is being evaluated by officials at PlantVillage for potential future deployment as an early warning system in East Africa. We release the code at https://github.com/maryam-tabar/CLIMATES.

Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch.This leads to a lack of generalization in practice and redundant computation.In particular, the state-of-the-art transformer models (e.g., BERT, RoBERTa) require great time and computation resources.By borrowing an idea from software engineering, in order to address these limitations, we propose a novel algorithm, SHIELD, which modifies and re-trains only the last layer of a textual NN, and thus it "patches" and "transforms" the NN into a stochastic weighted ensemble of multi-expert prediction heads.Considering that most of current black-box attacks rely on iterative search mechanisms to optimize their adversarial perturbations, SHIELD confuses the attackers by automatically utilizing different weighted ensembles of predictors depending on the input.In other words, SHIELD breaks a fundamental assumption of the attack, which is a victim NN model remains constant during an attack.By conducting comprehensive experiments, we demonstrate that all of CNN, RNN, BERT, and RoBERTa-based textual NNs, once patched by SHIELD, exhibit a relative enhancement of 15%-70% in accuracy on average against 14 different black-box attacks, outperforming 6 defensive baselines across 3 public datasets.Source code will be published at github.com/lethaiq/ shield-defend-adversarial-texts.

The widespread eviction of tenants across the United States has metamorphosed into a challenging public-policy problem. In particular, eviction exacerbates several income-based, educational, and health inequities in society, e.g., eviction disproportionately affects low-income renting families, many of whom belong to underrepresented minority groups. Despite growing interest in understanding and mitigating the eviction crisis, there are several legal and infrastructural obstacles to data acquisition at scale that limit our understanding of the distribution of eviction across the United States. To circumvent existing challenges in data acquisition, we propose WARNER, a novel Machine Learning (ML) framework that predicts eviction filing hotspots in US counties from unlabeled satellite imagery dataset. We account for the lack of labeled training data in this domain by leveraging sociological insights to propose a novel approach to generate probabilistic labels for a subset of an unlabeled dataset of satellite imagery, which is then used to train a neural network model to identify eviction filing hotspots. Our experimental results show that WARNER acheives a higher predictive performance than several strong baselines. Further, the superiority of WARNER can be generalized to different counties across the United States. Our proposed framework has the potential to assist NGOs and policymakers in designing well-informed (data-driven) resource allocation plans to improve the nationwide housing stability. This work is conducted in collaboration with The Child Poverty Action Lab (a leading non-profit leveraging data-driven approaches to inform actions for relieving poverty and relevant problems in Dallas County, TX). The code can be accessed via https://github.com/maryam-tabar/WARNER.

The goal of temporal knowledge graph embedding (TKGE) is to represent the entities and relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (i.e., embeddings), which preserve both semantic information and temporal dynamics of the factual information. In this paper, we posit that the intrinsic difficulty of existing TKGE methods lies in the lack of information in KG snapshots with timestamps, each of which contains the facts that co-occur at a specific timestamp. To address this challenge, we propose a novel self-supervised TKGE approach, THOR (Three-tower grapH cOnvolution netwoRks (GCNs)), which extracts latent knowledge from TKGs by jointly leveraging both temporal and atemporal dependencies between entities and the structural dependency between relations. THOR learns the embeddings of entities and relations Our experiments on three real-world datasets demonstrate that THOR significantly outperforms 13 competitors in terms of TKG completion tasks. The codebase of THOR is available at https://github.com/EJHyun/THOR.

Eviction of tenants has reached a crisis level in the U.S. and its consequences pose significant challenges to society. To tackle this eviction crisis, policymakers have been allocating financial resources but a more efficient resource allocation would need an accurate forecast of the number of tenants at-risk of evictions ahead of time. To help enhance the existing eviction prevention/diversion programs, in this work, we propose a multi-view deep neural network model, named as MARTIAN, that forecasts the number of tenants at-risk of getting formally evicted (at the census tract level) n months into the future. Then, we evaluate MARTIAN’s predictive performance under various conditions using real-world eviction cases filed across Dallas County, TX. The results of empirical evaluation show that MARTIAN outperforms an extensive set of baseline models in terms of predictive performance. Additionally, MARTIAN’s superior predictive performance is generalizable to unseen census tracts, for which no labeled data is available in the training set. This research has been done in collaboration with Child Poverty Action Lab (CPAL), which is a pioneering non-governmental organization (NGO) working for tackling poverty-related issues across Dallas County, TX. The usability of MARTIAN is under review by subject matter experts. We release our codebase at https://github.com/maryam-tabar/MARTIAN.

Health thread recommendation methods aim to suggest the most relevant existing threads for a user. Most of the existing methods tend to rely on modeling the post contents to retrieve relevant answers. However, some posts written by users with different clinical conditions can be lexically similar, as unrelated diseases (e.g., Angina and Osteoporosis) may have the same symptoms (e.g., back pain), yet irrelevant threads to a user. Therefore, it is critical to not only consider the connections between users and threads, but also the descriptions of users' symptoms and clinical conditions. In this paper, towards this problem of thread recommendation in online healthcare forums, we propose a knowledge graph enhanced Threads Recommendation (KETCH) model, which leverages graph neural networks to model the interactions among users and threads, and learn their representations. In our model, the users, threads and posts are three types of nodes in a graph, linked through their associations. KETCH uses the message passing strategy by aggregating information along with the network. In addition, we introduce a knowledge-enhanced attention mechanism to capture the latent conditions and symptoms. We also apply the method to the task of predicting the side effects of drugs, to show that KETCH has the potential to complement the medical knowledge graph. Comparing with the best results of seven competing methods, in terms of MRR, KETCH outperforms all methods by at least 0.125 on the MedHelp dataset, 0.048 on the Patient dataset and 0.092 on HealthBoards dataset, respectively. We release the source code of KETCH at: https://github.com/cuilimeng/KETCH.

Human labeling is time-consuming and costly. This problem is further exacerbated in extremely imbalanced class label scenarios, such as detecting fraudsters in online websites. Active learning selects the most relevant example for human labelers to improve the model performance at a lower cost. However, existing methods for active learning for graph data often assumes that both data and label distributions are balanced. These assumptions fail in extreme rare-class classification scenarios, such as classifying abusive reviews in an e-commerce website.

A large-batch training with data parallelism is a widely adopted approach to efficiently train a large deep neural network (DNN) model. Large-batch training, however, often suffers from the problem of the model quality degradation because of its fewer iterations. To alleviate this problem, in general, learning rate (lr) scaling methods have been applied, which increases the learning rate to make an update larger at each iteration. Unfortunately, however, we observe that large-batch training with state-of-the-art lr scaling methods still often degrade the model quality when a batch size crosses a specific limit, rendering such lr methods less useful. To this phenomenon, we hypothesize that existing lr scaling methods overlook the subtle but important differences across “layers” in training, which results in the degradation of the overall model quality. From this hypothesis, we propose a novel approach (LENA) toward the learning rate scaling for large-scale DNN training, employing: (1) a layer-wise adaptive lr scaling to adjust lr for each layer individually, and (2) a layer-wise state-aware warm-up to track the state of the training for each layer and finish its warm-up automatically. The comprehensive evaluation with variations of batch sizes demonstrates that LENA achieves the target accuracy (i.e., the accuracy of single-worker training): (1) within the fewest iterations across different batch sizes (up to 45.2% fewer iterations and 44.7% shorter time than the existing state-of-the-art method), and (2) for training very large-batch sizes, surpassing the limits of all baselines.

Socialbots are software-driven user accounts on social platforms, acting autonomously (mimicking human behavior), with the aims to influence the opinions of other users or spread targeted misinformation for particular goals. As socialbots undermine the ecosystem of social platforms, they are often considered harmful. As such, there have been several computational efforts to auto-detect the socialbots. However, to our best knowledge, the adversarial nature of these socialbots has not yet been studied. This begs a question “can adversaries, controlling socialbots, exploit AI techniques to their advantage?” To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected. We first formulate the adversarial socialbot learning as a cooperative game between two functional hierarchical RL agents. While one agent curates a sequence of activities that can avoid the detection, the other agent aims to maximize network influence by selectively connecting with right users. Our proposed policy networks train with a vast amount of synthetic graphs and generalize better than baselines on unseen real-life graphs both in terms of maximizing network influence (up to +18%) and sustainable stealthiness (up to +40% undetectability) under a strong bot detector (90% detection accuracy). During inference, the complexity of our approach scales linearly, independent of a network’s structure and the virality of news. This makes our attack very practical in a real-life setting.

Thai Le, Noseong Park, Dongwon Lee. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation. In this paper, we study how to achieve user-side fairness in bandit based recommendation. We formulate our fair personalized recommendation as a modified contextual bandit and focus on achieving fairness on the individual whom is being recommended an item as opposed to achieving fairness on the items that are being recommended. We introduce a metric that captures the fairness in terms of rewards received for both the privileged and protected groups. We develop a fair contextual bandit algorithm, Fair-LinUCB, that improves upon the traditional LinUCB algorithm to achieve group-level fairness of users. Our algorithm detects and monitors unfairness during personalized online recommendation. We provide a theoretical regret analysis and show that our algorithm has a slightly higher regret bound than LinUCB. We conduct numerous experimental evaluations to compare the performances of our fair contextual bandit to that of LinUCB and show that our approach achieves group-level fairness while maintaining a high utility.

Studies show that users do not reliably click more often on headlines classified as clickbait by automated classifiers. Is this because the linguistic criteria (e.g., use of lists or questions) emphasized by the classifiers are not psychologically relevant in attracting interest, or because their classifications are confounded by other unknown factors associated with assumptions of the classifiers? We address these possibilities with three studies—a quasi-experiment using headlines classified as clickbait by three machine-learning models (Study 1), a controlled experiment varying the headline of an identical news story to contain only one clickbait characteristic (Study 2), and a computational analysis of four classifiers using real-world sharing data (Study 3). Studies 1 and 2 revealed that clickbait did not generate more curiosity than non-clickbait. Study 3 revealed that while some headlines generate more engagement, the detectors agreed on a classification only 47% of the time, raising fundamental questions about their validity.

To speed up the training of massive deep neural network (DNN) models, distributed training has been widely studied. In general, a centralized training, a type of distributed training, suffers from the communication bottleneck between a parameter server (PS) and workers. On the other hand, a decentralized training suffers from increased parameter variance among workers that causes slower model convergence. Addressing this dilemma, in this work, we propose a novel centralized training algorithm, ALADDIN, employing "asymmetric" communication between PS and workers for the PS bottleneck problem and novel updating strategies for both local and global parameters to mitigate the increased variance problem. Through a convergence analysis, we show that the convergence rate of ALADDIN is O(1 ønk ) on the non-convex problem, where n is the number of workers and k is the number of training iterations. The empirical evaluation using ResNet-50 and VGG-16 models demonstrates that (1) ALADDIN shows significantly better training throughput with up to 191% and 34% improvement compared to a synchronous algorithm and the state-of-the-art decentralized algorithm, respectively, (2) models trained by ALADDIN converge to the accuracies, comparable to those of the synchronous algorithm, within the shortest time, and (3) the convergence of ALADDIN is robust under various heterogeneous environments.

In recent years, quantization methods have successfully accelerated the training of large deep neural network (DNN) models by reducing the level of precision in computing operations (e.g., forward/backward passes) without sacrificing its accuracy. In this work, therefore, we attempt to apply such a quantization idea to the popular Matrix factorization (MF) methods to deal with the growing scale of models and datasets in recommender systems. However, to our dismay, we observe that the state-of-the-art quantization methods are not effective in the training of MF models, unlike their successes in the training of DNN models. To this phenomenon, we posit that two distinctive features in training MF models could explain the difference: (i) the training of MF models is much more memory-intensive than that of DNN models, and (ii) the quantization errors across users and items in recommendation are not uniform. From these observations, we develop a quantization framework for MF models, named MASCOT, employing novel strategies (i.e., m-quantization and g-switching) to successfully address the aforementioned limitations of quantization in the training of MF models. The comprehensive evaluation using four real-world datasets demonstrates that MASCOT improves the training performance of MF models by about 45%, compared to the training without quantization, while maintaining low model errors, and the strategies and implementation optimizations of MASCOT are quite effective in the training of MF models. For the detailed information about MASCOT, we release the code of MASCOT and the datasets at: https://github.com/Yujaeseo/lCDM-2021_MASCOT.

We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. At the lower level, our neural networks consider a wide variety of features, such as classical air carrier performance features and transportation network features, to predict the market influence. On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2,262 routes. This problem falls into the category of the non-linear optimization problem, which cannot be solved exactly by conventional methods. To this end, we present a novel adaptive gradient ascent (AGA) method. Our prediction models show two to eleven times better accuracy in terms of the median root-mean-square error (RMSE) over baselines. In addition, our AGA optimization method runs 690 times faster with a better optimization result (in one of our largest scale experiments) than a greedy algorithm.

East Africa is experiencing the worst locust infestation in over 25 years, which has severely threatened the food security of millions of people across the region. The primary strategy adopted by human experts at the United Nations Food and Agricultural Organization (UN-FAO) to tackle locust outbreaks involves manually surveying at-risk geographical areas, followed by allocating and spraying pesticides in affected regions. In order to augment and assist human experts at the UN-FAO in this task, we utilize crowdsourced reports of locust observations collected by PlantVillage (the world's leading knowledge delivery system for East African farmers) and develop PLAN, a Machine Learning (ML) algorithm for forecasting future migration patterns of locusts at high spatial and temporal resolution across East Africa. PLAN's novel spatio-temporal deep learning architecture enables representing PlantVillage's crowdsourced locust observation data using novel image-based feature representations, and its design is informed by several unique insights about this problem domain. Experimental results show that PLAN achieves superior predictive performance against several baseline models - it achieves an AUC score of 0.9 when used with a data augmentation method. PLAN represents a first step in using deep learning to assist and augment human expertise at PlantVillage (and UN-FAO) in locust prediction, and its real-world usability is currently being evaluated by domain experts (including a potential idea to use the heatmaps created by PLAN in a Kenyan TV show). The source code is available at https://github.com/maryam-tabar/PLAN.

Minjin Choi, Sunkyung Lee, Eunseong Choi, Heesoo Park, Junhyuk Lee, Dongwon Lee, Jongwuk Lee. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

In this paper, we address the personalized node ranking (PNR) problem for signed networks, which aims to rank nodes in an order most relevant to a given seed node in a signed network. The recently-proposed PNR methods introduce the concept of the signed random surfer, denoted as SRSurfer, that performs the score propagation between nodes using the balance theory. However, in real settings of signed networks, edge relationships often do not strictly follow the rules of the balance theory. Therefore, SRSurfer-based PNR methods frequently perform incorrect score propagation to nodes, thereby degrading the accuracy of PNR. To address this limitation, we propose a novel random-walk based PNR approach with sign verification, named as OBOE (lOok Before yOu lEap). Specifically, OBOE carefully verifies the score propagation of SRSurfer by using the topological features of nodes. Then, OBOE corrects all incorrect score propagation cases by exploiting the statistics of a given network. The experiments on 3 real-world signed networks show that OBOE consistently and significantly outperforms 5 competing methods with improvement up to 13%, 95%, and 249% in top-k PNR, bottom-k PNR, and troll identification tasks, respectively. All OBOE codes and datasets are available at: http://github.com/wonchang24/OBOE.

Because of the increasingly negative impacts of the echo chamber effect, such as the dissemination of fake news and political polarization occurring in social networking services (SNSs), considerable efforts are being made to mitigate this effect. Prior HCI studies have presented the development of user interfaces to display information that reflects various standpoints, with the aim of nudging people to consume information in a more objective fashion. However, these efforts still lack the ability to highlight the characteristics, generation processes, and negative effects of echo chambers, so they may not be effective in helping people become sufficiently aware of the echo chamber effect and those who are already in an echo chamber. In this paper, we present ChamberBreaker (CB), which has been designed to help increase a player's awareness of and preemptively respond to an echo chamber effect based on psychological concepts: inoculation, heuristics for judging, and gamification. Through a user study with 882 participants (control group: 446, experimental group: 436), we demonstrated the feasibility of our game-based methodology to support the awareness of the echo chamber effect and the importance of maintaining diverse perspectives when consuming information. Our findings highlight the externalization of psychological standpoints in mitigating an echo chamber effect and suggest design implications for system development---the consideration of demographics, playing time, and the connection to fake news recognition---for digital literacy education. You can play CB at http://tiny.cc/chamberbreaker (The game only works with Chrome.)

The Multi-Task Learning (MTL) leverages the inter-relationship across tasks and is useful for applications with limited data. Existing works articulate different task relationship assumptions, whose validity is vital to successful multi-task training. We observe that, in many scenarios, the inter-relationship across tasks varies across different groups of data (i.e., topic), which we call within-topic task relationship hypothesis. In this case, current MTL models with homogeneous task relationship assumption cannot fully exploit different task relationships among different groups of data. Based on this observation, in this paper, we propose a generalized topic-wise multi-task architecture, to capture the within-topic task relationship, which can be combined with any existing MTL designs. Further, we propose a new specialized MTL design, topic-task-sparsity, along with two different types of sparsity constraints. The architecture, combined with the topic-task-sparsity design, constructs our proposed TOMATO model. The experiments on both synthetic and 4 real-world datasets show that our proposed models consistently outperform 6 state-of-the-art models and 2 baselines with improvement from $5%$ to $46%$ in terms of task-wise comparison, demonstrating the validity of the proposed within-topic task relationship hypothesis. We release the source codes and datasets of TOMATO at: https://github.com/JasonLC506/MTSEM.

In recent years, the task of generating realistic short and long texts have made tremendous advancements. In particular, several recently proposed neural network-based language models have demonstrated their astonishing capabilities to generate texts that are challenging to distinguish from human-written texts with the naked eye. Despite many benefits and utilities of such neural methods, in some applications, being able to tell the “author” of a text in question becomes critically important. In this work, in the context of this Turing Test, we investigate the so-called authorship attribution problem in three versions: (1) given two texts T1 and T2, are both generated by the same method or not? (2) is the given text T written by a human or machine? (3) given a text T and k candidate neural methods, can we single out the method (among k alternatives) that generated T? Against one humanwritten and eight machine-generated texts (i.e., CTRL, GPT, GPT2, GROVER, XLM, XLNET, PPLM, FAIR), we empirically experiment with the performance of various models in three problems. By and large, we find that most generators still generate texts significantly different from human-written ones, thereby making three problems easier to solve. However, the qualities of texts generated by GPT2, GROVER, and FAIR are better, often confusing machine classifiers in solving three problems. All codes and datasets of our experiments are available at: https://bit.ly/ 302zWdz

In recent years, the proliferation of so-called “fake news” has caused much disruptions in society and weakened the news ecosystem. Therefore, to mitigate such problems, researchers have developed state-of-the-art (SOTA) models to autodetect fake news on social media using sophisticated data science and machine learning techniques. In this work, then, we ask “what if adversaries attempt to attack such detection models?” and investigate related issues by (i) proposing a novel attack scenario against fake news detectors, in which adversaries can post malicious comments toward news articles to mislead SOTA fake news detectors, and (ii) developing Malcom, an end-to-end adversarial comment generation framework to achieve such an attack. Through a comprehensive evaluation, we demonstrate that about 94% and 93.5% of the time on average Malcom can successfully mislead five of the latest neural detection models to always output targeted real and fake news labels. Furthermore, Malcom can also fool black box fake news detectors to always output real news labels 90% of the time on average. We also compare our attack model with four baselines across two real-world datasets, not only on attack performance but also on generated quality, coherency, transferability, and robustness. We release the source code of Malcom at https://github.com/lethaiq/MALCOM1.

To provide accurate and explainable misinformation detection, it is often useful to take an auxiliary source (e.g., social context and knowledge base) into consideration. Existing methods use social contexts such as users' engagements as complementary information to improve detection performance and derive explanations. However, due to the lack of sufficient professional knowledge, users seldom respond to healthcare information, which makes these methods less applicable. In this work, to address these shortcomings, we propose a novel knowledge guided graph attention network for detecting health misinformation better. Our proposal, named as DETERRENT, leverages on the additional information from medical knowledge graph by propagating information along with the network, incorporates a Medical Knowledge Graph and an Article-Entity Bipartite Graph, and propagates the node embeddings through Knowledge Paths. In addition, an attention mechanism is applied to calculate the importance of entities to each article, and the knowledge guided article embeddings are used for misinformation detection. DETERRENT addresses the limitation on social contexts in the healthcare domain and is capable of providing useful explanations for the results of detection. Empirical validation using two real-world datasets demonstrated the effectiveness of DETERRENT. Comparing with the best results of eight competing methods, in terms of F1 Score, DETERRENT outperforms all methods by at least 4.78% on the diabetes dataset and 12.79% on cancer dataset. We release the source code of DETERRENT at: https://github.com/cuilimeng/DETERRENT.

Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats. To mitigate this limitation, therefore, we borrow two notable ideas (i.e., "explanation by intervention" from causality and "explanation are contrastive" from philosophy) and propose a novel solution, named as GRACE, that better explains neural network models' predictions for tabular datasets. In particular, given a model's prediction as label X, GRACE intervenes and generates a minimally-modified contrastive sample to be classified as Y, with an intuitive textual explanation, answering the question of "Why X rather than Y?" We carry out comprehensive experiments using eleven public datasets of different scales and domains (e.g., # of features ranges from 5 to 216) and compare GRACE with competing baselines on different measures: fidelity, conciseness, info-gain, and influence. The user-studies show that our generated explanation is not only more intuitive and easy-to-understand but also facilitates end-users to make as much as 60% more accurate post-explanation decisions than that of Lime.

Substance Use Disorder (SUD) is a devastating disease that leads to significant mental and behavioral impairments. Its negative effects damage the homeless youth population more severely (as compared to stably housed counterparts) because of their high-risk behaviors. To assist policymakers in devising effective and accurate long-term strategies to mitigate SUD, it is necessary to critically analyze environmental, psychological, and other factors associated with SUD among homeless youth. Unfortunately, there is no definitive data-driven study on analyzing factors associated with SUD among homeless youth. While there have been a few prior studies in the past, they (i) do not analyze variation in the associated factors for SUD with geographical heterogeneity in their studies; and (ii) only consider a few contributing factors to SUD in relatively small samples. This work aims to fill this gap by making the following three contributions: (i) we use a real-world dataset collected from ~1,400 homeless youth (across six American states) to build accurate Machine Learning (ML) models for predicting the susceptibility of homeless youth to SUD; (ii) we find a representative set of factors associated with SUD among this population by analyzing feature importance values associated with our ML models; and (iii) we investigate the effect of geographical heterogeneity on the factors associated with SUD. Our results show that our system using adaptively boosted decision trees achieves the best predictive accuracy out of several algorithms on the SUD prediction task, achieving an Area Under the ROC Curve of 0.85. Further, among other things, we also find that both Post-Traumatic Stress Disorder (PTSD) and depression are very strongly associated with SUD among homeless youth because of their propensity to self-medicate to alleviate stress. This work is done in collaboration with social work scientists, who are currently evaluating the results for potential future deployment.

How to effectively detect fake news and prevent its diffusion on social media has gained much attention in recent years. However, relatively little focus has been given on exploiting user comments left for posts and latent sentiments therein in detecting fake news. Inspired by the rich information available in user comments on social media, therefore, we investigate whether the latent sentiments hidden in user comments can potentially help distinguish fake news from reliable content. We incorporate users' latent sentiments into an end-to-end deep embedding framework for detecting fake news, named as SAME. First, we use multi-modal networks to deal with heterogeneous data modalities. Second, to learn semantically meaningful spaces per data source, we adopt an adversarial mechanism. Third, we define a novel regularization loss to bring embeddings of relevant pairs closer. Our comprehensive validation using two real-world datasets, PolitiFact and GossipCop, demonstrates the effectiveness of SAME in detecting fake news, significantly outperforming state-of-the-art methods.

Clickbait is an attractive yet misleading headline that lures readers to commit click-conversion. Development of robust clickbait detection models has been, however, hampered due to the shortage of high-quality labeled training samples. To overcome this challenge, we investigate how to exploit human-written and machine-generated synthetic clickbaits. We first ask crowdworkers and journalism students to generate clickbaity news headlines. Second, we utilize deep generative models to generate clickbaity headlines. Through empirical evaluations, we demonstrate that synthetic clickbaits by human entities and deep generative models are consistently useful in improving the accuracy of various prediction models, by as much as 14.5% in AUC, across two real datasets and different types of algorithms. Especially, we observe an improvement in accuracy, up to 8.5% in AUC, even for top-ranked clickbait detectors from Clickbait Challenge 2017. Our study proposes a novel direction to address the shortage of labeled training data, one of fundamental bottlenecks in supervised learning, by means of synthetic training data with reinforced domain knowledge. It also provides a solution for distinguishing between bot-generated and human-written clickbaits, thus aiding the work of moderators and better alerting news consumers.

As one of the fundamental tasks in data analytics, Influence Maximization methods have been widely used in many real-world applications. For instance, in social network analysis, after building a directed graph, where edges are weighted with influence probabilities, influence maximization methods can be used to find a set of users who can maximize the spread of information under certain cascade models. Despite their successes, however, one critical weakness of existing influence maximization methods lies in the fact that edges are weighted with historical probabilities. As such, influence maximization methods perform sub-optimal if there occur non-trivial changes in future. In response to this challenge, in this work, we propose a novel prediction-driven influence maximization method that accurately predicts future influence probabilities using graph convolutional networks and find seed users based on the predicted probabilities. The experiments with five real-world datasets show that our prediction accuracy is accurate (e.g., mean absolute percentage error less than 0.1) in many cases, and our prediction-driven influence maximization is very close to the optimal.

Despite recent advancements in computationally detecting fake news, we argue that a critical missing piece be the explainability of such detection--i.e., why a particular piece of news is detected as fake--and propose to exploit rich information in users' comments on social media to infer the authenticity of news. In this demo paper, we present our system for an explainable fake news detection called dEFEND, which can detect the authenticity of a piece of news while identifying user comments that can explain why the news is fake or real. Our solution develops a sentence-comment co-attention sub-network to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and user comments for fake news detection. The system is publicly accessible.

In recent years, to mitigate the problem of fake news, computational detection of fake news has been studied, producing some promising early results. While important, however, we argue that a critical missing piece of the study be the explainability of such detection, i.e., why a particular piece of news is detected as fake. In this paper, therefore, we study the explainable detection of fake news. We develop a sentence-comment co-attention sub-network to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and user comments for fake news detection. We conduct extensive experiments on real-world datasets and demonstrate that the proposed method not only significantly outperforms 7 state-of-the-art fake news detection methods by at least 5.33% in F1-score, but also (concurrently) identifies top-k user comments that explain why a news piece is fake, better than baselines by 28.2% in NDCG and 30.7% in Precision.

Detecting local graph clusters is an important problem in big graph analysis. Given seed nodes in a graph, local clustering aims at finding subgraphs around the seed nodes, which consist of nodes highly relevant to the seed nodes. However, existing local clustering methods either allow only a single seed node, or assume all seed nodes are from the same cluster, which is not true in many real applications. Moreover, the assumption that all seed nodes are in a single cluster fails to use the crucial information of relations between seed nodes. In this paper, we propose a method to take advantage of such relationship. With prior knowledge of the community membership of the seed nodes, the method labels seed nodes in the same (different) community by the same (different) color. To further use this information, we introduce a color-based random walk mechanism, where colors are propagated from the seed nodes to every node in the graph. By the interaction of identical and distinct colors, we can enclose the supervision of seed nodes into the random walk process. We also propose a heuristic strategy to speed up the algorithm by more than 2 orders of magnitude. Experimental evaluations reveal that our clustering method outperforms state-of-the-art approaches by a large margin.

Abstract With the growing popularity of online social networks, a large amount of private or sensitive information has been posted online. In particular, studies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. As such, there exist great needs to be able to identify potentially-sensitive online contents, so that users could be alerted with such findings. In this paper, we propose a context-aware, text-based quantitative model for private information assessment, namelyPrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first solicit diverse opinions on the sensitiveness of private information from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to generate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs.

We develop a novel framework, named as$l$-injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-Nrecommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion ofpre-use preferencesof users toward a vast amount ofunrateditems. Using this notion, we identifyuninterestingitems that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in the experiments are available at:https://goo.gl/KUrmip.

We investigate how to address the shortcomings of the popular One-Class Collaborative Filtering (OCCF) methods in handling challenging “sparse” dataset in one-class setting (e.g., clicked or bookmarked), and propose a novel graph-theoretic OCCF approach, named as gOCCF, by exploiting both positive preferences (derived from rated items) as well as negative preferences (derived from unrated items). In capturing both positive and negative preferences as a bipartite graph, further, we apply the graph shattering theory to determine the right amount of negative preferences to use. Then, we develop a suite of novel graph-based OCCF methods based on the random walk with restart and belief propagation methods. Through extensive experiments using 3 real-life datasets, we show that our gOCCF effectively addresses the sparsity challenge and significantly outperforms all of 8 competing methods in accuracy on very sparse datasets while providing comparable accuracy to the best performing OCCF methods on less sparse datasets. The datasets and implementations used in the empirical validation are available for access: https://goo.gl/sfiawn.

Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked. In experimental validation, the RME outperforms competing state-of-the-art models in both explicit and implicit feedback datasets, significantly improving [email protected] by 5.9~7.0%, [email protected] by 4.3~5.6%, and [email protected] by 7.9~8.9%. In addition, under the cold-start scenario for users with the lowest number of interactions, against the competing models, the RME outperforms [email protected] by 20.2% and 29.4% in MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source code are available at: https://github.com/thanhdtran/RME.git.

Task assignment is a central component in crowdsourcing. Organizational studies have shown that worker motivation in completing tasks has a direct impact on the quality of individual contributions. In this work, we examine motivation-aware task assignment in the presence of a set of workers. We propose to model motivation as a balance between task relevance and task diversity and argue that an adaptive approach to task assignment can best capture the evolving nature of motivation. Worker motivation is observed and task assignment is revisited appropriately across iterations. We prove the problem to be NP-hard as well as MaxSNP-Hard and develop efficient approximation algorithms with provable guarantees. Our experiments with synthetic data examine the scalability of our algorithms, and our live real data experiments show that capturing motivation using relevance and diversity leads to high crowdwork quality.

Clickbaits are catchy social posts or sensational headlines that attempt to lure readers to click. Clickbaits are pervasive on social media and can have significant negative impacts on both users and media ecosystems. For example, users may be misled to receive inaccurate information or fall into click-jacking attacks. Similarly, media platforms could lose readers' trust and revenues due to the prevalence of clickbaits. To computationally detect such clickbaits on social media using a supervised learning framework, one of the major obstacles is the lack of large-scale labeled training data, due to the high cost of labeling. With the recent advancements of deep generative models, to address this challenge, we propose to generate synthetic headlines with specific styles and explore their utilities to help improve clickbait detection. In particular, we propose to generate stylized headlines from original documents with style transfer. Furthermore, as it is non-trivial to generate stylized headlines due to several challenges such as the discrete nature of texts and the requirements of preserving semantic meaning of document while achieving style transfer, we propose a novel solution, named as Stylized Headline Generation (SHG), that can not only generate readable and realistic headlines to enlarge original training data, but also help improve the classification capacity of supervised learning. The experimental results on real-world datasets demonstrate the effectiveness of SHG in generating high-quality and high-utility headlines for clickbait detection.

With the dramatically increasing participation in online social networks (OSNs), huge amount of private information becomes available on such sites. It is critical to preserve users' privacy without preventing them from socialization and sharing. Unfortunately, existing solutions fall short meeting such requirements. We argue that the key component of OSN privacy protection is protecting (sensitive) content -- privacy as having the ability to control information dissemination. We follow the concepts of private information boundaries and restricted access and limited control to introduce a social circle model. We articulate the formal constructs of this model and the desired properties for privacy protection in the model. We show that the social circle model is efficient yet practical, which provides certain level of privacy protection capabilities to users, while still facilitates socialization. We then utilize this model to analyze the most popular social network platforms on the Internet (Facebook, Google+, WeChat, etc), and demonstrate the potential privacy vulnerabilities in some social networks. Finally, we discuss the implications of the analysis, and possible future directions.

International audience

As the commercial implications of Likes in online social networks multiply, the number of fake Likes also increase rapidly. To maintain a healthy ecosystem, however, it is critically important to prevent and detect such fake Likes. Toward this goal, in this paper, we investigate the problem of detecting the so-called "fake likers" who frequently make fake Likes for illegitimate reasons. To uncover fake Likes in online social networks, we: (1) first collect a substantial number of profiles of both fake and legitimate Likers using linkage and honeypot approaches, (2) analyze the characteristics of both types of Likers, (3) identify effective features exploiting the learned characteristics and apply them in supervised learning models, and (4) thoroughly evaluate their performances against three baseline methods and under two attack models. Our experimental results show that our proposed methods with effective features significantly outperformed baseline methods, with accuracy = 0.871, false positive rate = 0.1, and false negative rate = 0.14.

In this paper, we propose a crowdsourcing-based approach to solving skyline queries with incomplete data. Our main idea is to leverage crowds to infer the pair-wise preferences between tuples when the values of tuples in some attributes are unknown. Specifically, our proposed solution considers three key factors used in existing crowd-enabled algorithms: (1) minimizing a monetary cost in identifying a crowdsourced skyline by using a dominating set, (2) reducing the number of rounds for latency by parallelizing the questions asked to crowds, and (3) improving the accuracy of a crowdsourced skyline by dynamically assigning the number of crowd workers per question. We evaluate our solution over both simulated and real crowdsourcing using the Amazon Mechanical Turk. Compared to a sort-based baseline method, our solution significantly minimizes the monetary cost, and reduces the number of rounds up to two orders of magnitude. In addition, our dynamic majority voting method shows higher accuracy than both static majority voting method and the existing solution using unary questions.

We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in experiments are available at: https://goo.gl/KUrmip.

The emerging fields of social computing and computational social science have been driven by the transdisciplinary efforts of scientists, researchers, and scholars from many fields of study. Collaborations across the social, natural, physical, information, computer, mathematical, and health sciences in addition to the humanities have resulted in significant advancements in our understanding of human behavior.

The emergence of social media has had a significant impact on how people communicate and socialize. Teens use social media to make and maintain social connections with friends and build their reputation. However, the way of analyzing the characteristics of teens in social media has mostly relied on ethnographic accounts or quantitative analyses with small datasets. This paper shows the possibility of detecting age information in user profiles by using a combination of textual and facial recognition methods and presents a comparative study of 27K teens and adults in Instagram. Our analysis highlights that (1) teens tend to post fewer photos but highly engage in adding more tags to their own photos and receiving more Likes and comments about their photos from others, and (2) to post more selfies and express themselves more than adults, showing a higher sense of self-representation. We demonstrate the application of our novel method that shows clear trends of age differences as well as substantiates previous insights in social media.

In this 3-hour tutorial, we present the landscape of recent developments in data management and mining research, and survey a selected set of state-of-the-art works that significantly extended existing database reserach in order to incorporate and exploit the novel notion of “crowdsourcing” in a creative fashion. In particular, three speakers take turns to present the topics of human-powered database operations, crowdsourced data mining, and the application of crowdsourcing in social media, respectively.

The problem of Twitter user classification using the contents of tweets is studied. We generate time series from tweets by exploiting the latent temporal information and solve the classification problem in time series domain. Our approach is inspired by the fact that Twitter users sometimes exhibit the periodicity pattern when they share their activities or express their opinions. We apply our proposed methods to both binary and multi-class classification of sports and political interests of Twitter users and compare the performance against eight conventional classification methods using textual features. Experimental results using 2.56 million tweets show that our best binary and multi-class approaches improve the classification accuracy over the best baseline binary and multi-class approaches by 15% and 142%, respectively.

As large-scale text data become available on the Web, textual errors in a corpus are often inevitable (e.g., digitizing historic documents). Due to the calculation of frequencies of words, however, such textual errors can significantly impact the accuracy of statistical models such as the popular Latent Dirichlet Allocation (LDA) model. To address such an issue, in this paper, we propose two novel extensions to LDA (i.e., TE-LDA and TDE-LDA): (1) The TE-LDA model incorporates textual errors into term generation process; and (2) The TDE-LDA model extends TE-LDA further by taking into account topic dependency to leverage on semantic connections among consecutive words even if parts are typos. Using both real and synthetic data sets with varying degrees of "errors", our TDE-LDA model outperforms: (1) the traditional LDA model by 16%-39% (real) and 20%-63% (synthetic); and (2) the state-of-the-art N-Grams model by 11%-27% (real) and 16%-54% (synthetic).

Today's organizations raise an increasing need for information sharing via on-demand access. Information brokering systems (IBSs) have been proposed to connect large-scale loosely federated data sources via a brokering overlay, in which the brokers make routing decisions to direct client queries to the requested data servers. Many existing IBSs assume that brokers are trusted and thus only adopt server-side access control for data confidentiality. However, privacy of data location and data consumer can still be inferred from metadata (such as query and access control rules) exchanged within the IBS, but little attention has been put on its protection. In this paper, we propose a novel approach to preserve privacy of multiple stakeholders involved in the information brokering process. We are among the first to formally define two privacy attacks, namely attribute-correlation attack and inference attack, and propose two countermeasure schemes automaton segmentation and query segment encryption to securely share the routing decision-making responsibility among a selected set of brokering servers. With comprehensive security analysis and experimental results, we show that our approach seamlessly integrates security enforcement with query routing to provide system-wide security with insignificant overhead.

We study the problem of predicting home locations of Twitter users using contents of their tweet messages. Using three probability models for locations, we compare both the Gaussian Mixture Model (GMM) and the Maximum Likelihood Estimation (MLE). In addition, we propose two novel unsupervised methods based on the notions of Non-Localness and Geometric-Localness to prune noisy data from tweet messages. In the experiments, our unsupervised approach improves the baselines significantly and shows comparable results with the supervised state-of-the-art method. For 5,113 Twitter users in the test set, on average, our approach with only 250 selected local words or less is able to predict their home locations (within 100 miles) with the accuracy of 0.499, or has 509.3 miles of average error distance at best.

How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.

Location context in social media plays an important role in many applications. In addition to explicit location sharing via popular "check in" service, user-posted content could also implicitly reveals users' location context. Identifying such a location context based on content is an interesting problem because it is not only important in inferring social ties between people, but also vital for applications such as user profiling and targeted advertising. In this paper, we study the problem of location type classification using tweet content. We extend probabilistic text classification models to incorporate temporal features and user history information in terms of probabilistic priors. Experimental results show that our extensions can boost classification accuracy effectively.

Current QoS-aware automatic service composition queries over a network of Web services are often one-time innature. After a network of Web services is built, such queries are issued once, and answers are found from the scratch. The underlying assumption is that the participating Web services are rather static so that their functional and non-functional parameters seldom change. However, such an assumption is often baseless. New services come and go, service APIs change gradually, and QoS values fluctuate. Therefore, a support for efficiently handling "continuous" service composition queries is desired. In this paper, we propose an event driven continuous query algorithm for QoS-aware automatic service composition problem to cope with different types of dynamic services. Moreover, we integrated this algorithm in our service composition system, QSynth. Finally, we evaluate our proposal using both real QoS data and synthetic Web service data and show the superior performance of ours, compared to the state-of-the art solution which won the performance championship of Web Service Challenge in 2009 and 2010.

While the usage of web services has increased explosively in recent years, very few studies examined the characteristics of web services using large-scale real data for a long period of time. In this paper, we present one such a large scale longitudinal analysis of publicly available web services of SOAP-based and RESTful types. For the period of roughly one year and from five different world-wide locations, we closely monitor the ups and downs of various basic properties of web services and their QoS values using a total of 825,132 real web services.

We introduce a generative probabilistic document model based on latent Dirichlet allocation (LDA), to deal with textual errors in the document collection. Our model is inspired by the fact that most large-scale text data are machine-generated and thus inevitably contain many types of noise. The new model, termed as TE-LDA, is developed from the traditional LDA by adding a switch variable into the term generation process in order to tackle the issue of noisy text data. Through extensive experiments, the efficacy of our proposed model is validated using both real and synthetic data sets.

In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists’ standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists’ standpoints.

We study the performance issue of the "iterative" record linkage (RL) problem, where match and merge operations may occur together in iterations until convergence emerges. We first propose the Iterative Locality-Sensitive Hashing (ILSH) that dynamically merges LSH-based has tables for quick and accurate blocking. Then, by exploiting inherent characteristics within/across data sets, we develop a suite of I-LSH-based RL algorithms, named as HARRA (HAshed RecoRd linkAge). The superiority of HARRA in speed over competing RL solutions is thoroughly validated using various real data sets. While maintaining equivalent or comparable accuracy levels, for instance, HARRA runs: (1) 4.5 and 10.5 times faster than StringMap and R-Swoosh in iteratively linking 4,000 x 4,000 short records (i.e., one of the small test cases), and (2) 5.6 and 3.4 times faster than basic LSH and Multi-Probe LSH algorithms in iteratively linking 400,000 x 400,000 long records (i.e., the largest test case).

High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dimensional data in an optimal low-dimensional subspace, which is learned with a few user supplied constraints as well as the structure of input data. We study two types of constraints that indicate whether or not pairs of data points originate from the same class. Data partitions that satisfy both types of constraints may be conflicting. To solve this problem, our method projects data into two different subspaces, one in the kernel space and one in the original input space, each is designed for enforcing one type of constraints. Projections in the two spaces interact and data are embedded in an optimal low-dimensional subspace where constraints are maximally satisfied. Besides constraints, our method also preserves the intrinsic data structure, such that nearby/far away data points in the original space are still near to/far from each other in the embedded space. Compared to existing techniques, our method has the following advantages: 1) It can benefit from constraints even when only a few are available. 2) It is robust and does not suffer from over fitting. 3) It handles nonlinearly separable data, but learns a linear data transformation. Thus the method can be easily generalized to new data points and is efficient in dealing with large data sets. Experiments on real data from multiple domains clearly demonstrate that significant improvements in learning accuracy can be achieved after dimensionality reduction by employing only a few constraints.

As online social networks get more popular, it becomes increasingly critical to preserve user privacy in such networks. In this paper, we propose our preliminary results on defining and tackling information aggregation attacks over online social networks. We first introduce three major threats towards private information in online social networks. We conceptually model private information into multilevel and discretionary models. Then, we articulate information aggregation attacks under discretionary model. Finally, we present our preliminary design of "privacy monitor," a framework that allows users to define their own privacy scheme, and track their actual privacy disclosure to check for any unwanted leakage.

Matching two images with similar contents is one of the most fundamental tasks in image processing. Due to its importance, in recent years, many novel techniques have been proposed with great successes. Toward this effort, in this paper, we propose a radically different idea by bridging two seemingly unrelated fields - Image Processing and Biology - i.e., we propose to use the popular gene sequence alignment algorithm in Biology, BLAST, in determining the similarity between images. In this proposal, we map image features to a sequence of gene alphabets (e.g., A, C, G, and T in DNA, or 23 letters in protein) to utilize a wealth of advanced algorithms and tools in BLAST. Under the new idea, in particular, we study various image features and gene sequence generation methods that impact the accuracy and performance in matching similar images. Our proposal, termed as BLASTed Image Matching (BIM), is empirically validated using real data sets. Our work can be viewed as the ¿first¿ step toward bridging Image Processing and Biology fields in the application of the well-studied image matching problem.

The behavioral description based Web Service Composition (WSC) problem aims at the automatic construction of a coordinator web service that controls a set of web services to reach a goal state. However, solving the WSC problem exactly with a realistic model is doubly-exponential in the number of variables in web service descriptions. In this paper, we propose a novel efficient approximation-based algorithm using automatic abstraction and refinement to dramatically reduce the number of variables needed to solve the problem.

The Web service composition (WSC) problem on behavioral descriptions deals with the automatic construction of a coordinator web service to control a set of web services to reach the goal states. As such, WSC is one of the fundamental techniques to enable the Service Oriented Architecture on the Web. Despite its importance and implications, however, very few studies exist on the computational complexities of the WSC problem. In this paper, we present two novel theoretical findings on WSC problems: (1) Solving the WSC problem with "complete" information is EXP-hard, and (2) Solving the WSC problem with "incomplete" information is 2-EXP-hard. These findings imply that more efforts to devise efficient approximate solutions to the WSC problem be needed.

We present our vision and preliminary design toward Web-crawled academic video search engine, named as LeeDeo, that can search, crawl, archive, index, and browse ldquoacademicrdquo videos from the Web. Our proposal differs from existing general-purpose search engines such as Google or MSN whose focus is on the search of textual HTML documents or metadata of multimedia objects. Similarly, our proposal also differs from existing academic bibliographic search engines such as CiteSeer, arXiv, or Google Scholar whose focus is on the search and analysis of PDF or PS documents of academic papers. As desiderata of such an academic video search engine, we discuss various issues as follows: (1) Crawling: how to crawl, identify, and download academic videos from the Web? (2) Classification: how to determine the so-called academic videos from the rest? (3) Extraction: how to extract metadata and transcripts from the classified videos? (4) Indexing: how to build indexes for search engines? and (5) Interface: how to provide interface for efficient browse and search of academic videos?

The main research focus of Web services is to achieve the interoperability between distributed and heterogeneous applications. Therefore, flexible composition of Web services to fulfill the given challenging requirements is one of the most important objectives in this research field. However, until now, service composition has been largely an error-prone and tedious process. Furthermore, as the number of available web services increases, finding the right Web services to satisfy the given goal becomes intractable. In this paper, toward these issues, we propose an AI planning-based framework that enables the automatic composition of Web services, and explore the following issues. First, we formulate the Web-service composition problem in terms of AI planning and network optimization problems to investigate its complexity in detail. Second, we analyze publicly available Web service sets using network analysis techniques. Third, we develop a novel Web-service benchmark tool called WSBen. Fourth, we develop a novel AI planning-based heuristic Web-service composition algorithm named WSPR. Finally, we conduct extensive experiments to verify WSPR against state-of-the-art AI planners. It is our hope that both WSPR and WSBen will provide useful insights for researchers to develop Web-service discovery and composition algorithms, and software.

A novel microarray value imputation method, HICCUP1, is presented. HICCUP improves upon existing value imputation methods in the several ways. (1) By judiciously integrating heterogeneous microarray datasets using hierarchical clustering, HICCUP overcomes the limitation of using only single dataset with limited number of samples; (2) Unlike local or global value imputation methods, by mining association rules, HICCUP selects appropriate subsets of the most relevant samples for better value imputation; and (3) by exploiting relationship among the sample space (e.g., cancer vs. non-cancer samples), HICCUP improves the accuracy of value imputation. Experiments with a real prostate cancer microarray dataset verify that HICCUP outperforms existing approaches.

Materialized XPath access-control views are commonly used for enforcing access control. When access control rules defining a materialized XML access-control view change, the view must be adapted to reflect these changes. The process of updating a materialized view after its definition changes is referred to as view adaptation. While XPath security views have been widely reported in literature, the problem of view adaptation for XPath security views has not been addressed. View adaptation results in view downtime during which users are denied access to security views to prevent unauthorized access. Thus, efficient view adaptation is important for making XPath security views pragmatic. In this work, we show how to adapt an XPath access-control view incrementally by re-using the existing view, which reduces computation and communication costs significantly, and results in less downtime for the end-user. Empirical evaluations confirm that the incremental view adaptation algorithms presented in this paper are efficient and scalable.

A Distributed Information Brokering System (DIBS) is a peer-to-peer overlay network that comprises diverse data servers and brokering components helping client queries locate the data server(s). Many existing information brokering systems adopt server side access control deployment and honest assumptions on brokers. However, little attention has been drawn on privacy of data and metadata stored and exchanged within DIBS. In this paper, we address privacy-preserving information sharing via on-demand information access. We propose a flexible and scalable system using a broker-coordinator overlay network. Through an innovative automaton segmentation scheme, distributed access control enforcement, and query segment encryption, our system integrates security enforcement and query forwarding while preserving system-wide privacy. We present the automaton segmentation approach, analyze privacy preservation in details, and finally examine the end-to-end performance and scalability through experiments and analysis.

We study the parallelization of the (record) linkage problem - i.e., to identify matching records between two collections of records, A and B. One of main idiosyncrasies of the linkage problem, compared to Database join, is the fact that once two records a in A and b in B are matched and merged to c, c needs to be compared to the rest of records in A and B again since it may incur new matching. This re-feeding stage of the linkage problem requires its solution to be iterative, and complicates the problem significantly. Toward this problem, we first discuss three plausible scenarios of inputs - when both collections are clean, only one is clean, and both are dirty. Then, we show that the intricate interplay between match and merge can exploit the characteristics of each scenario to achieve good parallelization. Our parallel algorithms achieve 6.55-7.49 times faster in speedup compared to sequential ones with 8 processors, and 11.15-18.56% improvement in efficiency compared to P-Swoosh.

Poor quality data is prevalent in databases due to a variety of reasons, including transcription errors, lack of standards for recording database fields, etc. To be able to query and integrate such data, considerable recent work has focused on the record linkage problem, i.e., determine if two entities represented as relational records are approximately the same. Often entities are represented as groups of relational records, rather than individual relational records, e.g., households in a census survey consist of a group of persons. We refer to the problem of determining if two entities represented as groups are approximately the same as group linkage. Intuitively, two groups can be linked to each other if (i) there is high enough similarity between "matching" pairs of individual records that constitute the two groups, and (ii) there is a large fraction of such matching record pairs. In this paper, we formalize this intuition and propose a group linkage measure based on bipartite graph matching. Given a data set consisting of a large number of groups, efficiently finding groups with a high group linkage similarity to an input query group requires quickly eliminating the many groups that are unlikely to be desired matches. To enable this task, we present simpler group similarity measures that can be used either during fast pre-processing steps or as approximations to our proposed group linkage measure. These measures can be easily instantiated using SQL, permitting our techniques to be implemented inside the database system itself. We experimentally validate the utility of our measures and techniques using a variety of real and synthetic data sets.

When non-unique values are used as the identifier of entities, due to their homonym, confusion can occur.In particular, when (part of) "names" of entities are used as their identifier, the problem is often referred to as the name disambiguation problem, where goal is to sort out the erroneous entities due to name homonyms (e.g., if only last name is used as the identifier, one cannot distinguish "Vannevar Bush" from "George Bush").In this paper, in particular, we study the scalability issue of the name disambiguation problem -when (1) a small number of entities with large contents or (2) a large number of entities get un-distinguishable due to homonyms, how to resolve it?We first carefully examine two of the state-of-the-art solutions to the name disambiguation problem, and point out their limitations with respect to scalability.Then, we adapt the multi-level graph partition technique to solve the large-scale name disambiguation problem.Our claim is empirically validated via experimentation -our proposal shows orders of magnitude improvement in terms of performance while maintaining equivalent or reasonable accuracy compared to competing solutions.Formally, using the terms of Table 1, the name disambiguation problem in our setting is defined as follows: Downloaded 08/15/23 to 181.

The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.

A novel benchmark, WSBen, for testing web services discovery and composition is presented. WSBen includes: (1) A collection of synthetic web services (WSDL) files with diverse characteristics and sizes; (2) Test discovery and composition queries and solutions; and (3) External files for statistical analysis and AI planners. Users can fine-tune the generated WSDL files using various parameters such as skewness or matching type. It is our hope that WSBen will provide useful insights for researchers evaluating the performance of web services discovery and composition algorithms and softwares.

In this paper, we present a "value mapping" algorithm that does not rely on syntactic similarity or semantic interpretation of the values. The algorithm first constructs a statistical model (e.g., co-occurrence frequency or entropy vector) that captures the unique characteristics of values and their co-occurrence. It then finds the matching values by computing the distances between the models while refining the models using user feedback through iterations. Our experimental results suggest that our approach successfully establishes value mappings even in the presence of opaque data values and thus can be a useful addition to the existing data integration techniques.

On the basis of regular tree grammars, we present a formal framework for XML schema languages. This framework helps to describe, compare, and implement such schema languages in a rigorous manner. Our main results are as follows: (1) a simple framework to study three classes of tree languages (local, single-type, and regular); (2) classification and comparison of schema languages (DTD, W3C XML Schema, and RELAX NG) based on these classes; (3) efficient document validation algorithms for these classes; and (4) other grammatical concepts and advanced validation algorithms relevant to an XML model (e.g., binarization, derivative-based validation).

At present, most of the state-of-the-art solutions for XML access controls are either (1) document-level access control techniques that are too limited to support fine-grained security enforcement; (2) view-based approaches that are often expensive to create and maintain; or (3) impractical proposals that require substantial security-related support from underlying XML databases. In this paper, we take a different approach that assumes no security support from underlying XML databases and examine three alternative fine-grained XML access control solutions, namely primitive, pre-processing and post-processing approaches. In particular, we advocate a pre-processing method called QFilter that uses Non-deterministic Finite Automata (NFA) to rewrite user's query such that any parts violating access control rules are pruned. We show the construction and execution of a QFilter and demonstrate its superiority to other competing methods.

Two algorithms, called NeT and CoT, to translate relational schemas to XML schemas using various semantic constraints are presented. The XML schema representation we use is a language-independent formalism named XSchema, that is both precise and concise. A given XSchema can be mapped to a schema in any of the existing XML schema language proposals. Our proposed algorithms have the following characteristics: (1) NeT derives a nested structure from a flat relational model by repeatedly applying the nest operator on each table so that the resulting XML schema becomes hierarchical, and (2) CoT considers not only the structure of relational schemas, but also semantic constraints such as inclusion dependencies during the translation. It takes as input a relational schema where multiple tables are interconnected through inclusion dependencies and converts it into a good XSchema. To validate our proposals, we present experimental results using both real schemas from the UCI repository and synthetic schemas from TPC-H.

Two conversion algorithms, called NeT and COT, to translate relational schemas to XML schemas using various semantic constraints are presented. We first present a language-independent formalism named XSchema so that our algorithms are able to generate output schema in various XML schema language proposals. The benefits of such a formalism are that it is both precise and concise. Based on the XSchema formalism, our proposed algorithms have the following characteristics: (1) NeT derives a nested structure from a flat relational model by repeatedly applying the nest operator so that the resulting XML schema becomes hierarchical, and (2) COT considers not only the structure of relational schemas, but also inclusion dependencies during the translation so that relational schemas where multiple tables are interconnected through inclusion dependencies can also be handled.

A semantic caching scheme suitable for wrappers wrapping web sources is presented. Since the web sources have typically weaker querying capabilities than conventional databases, existing semantic caching schemes cannot be applied directly. A seamlessly integrated query translation and capability mapping between the wrappers and web sources in semantic caching is described. In addition, an analysis on the match types between the user's input query and cached queries is presented. Semantic knowledge acquired from the data can be used to avoid unnecessary access to the web sources by transforming the cache miss to the cache hit. A polynomial time algorithm based on the proposed query matching technique is presented to find the best matched query in the cache. Experimental results reveal the effectiveness of the proposed semantic caching scheme.