Fake news detection plays a crucial role in protecting social media users and maintaining a healthy news ecosystem. Among existing works, comment-based fake news detection methods are empirically shown as promising because comments could reflect users' opinions, stances, and emotions and deepen models' understanding of fake news. Unfortunately, due to exposure bias and users' different willingness to comment, it is not easy to obtain diverse comments in reality, especially for early detection scenarios. Without obtaining the comments from the "silent'' users, the perceived opinions may be incomplete, subsequently affecting news veracity judgment. In this paper, we explore the possibility of finding an alternative source of comments to guarantee the availability of diverse comments, especially those from silent users. Specifically, we propose to adopt large language models (LLMs) as a user simulator and comment generator, and design GenFEND, a generated feedback-enhanced detection framework, which generates comments by prompting LLMs with diverse user profiles and aggregating generated comments from multiple subpopulation groups. Experiments demonstrate the effectiveness of GenFEND and further analysis shows that the generated comments cover more diverse users and could even be more effective than actual comments.

Dialogue State Tracking (DST) module is an essential component of task-oriented dialog systems to understand users’ goals and needs. Collecting dialogue state labels including slots and values can be costly, requiring experts to annotate all (slot, value) information for each turn in dialogues. It is also difficult to define all possible slots and values in advance, especially with the wide application of dialogue systems in more and more new-rising applications. In this paper, we focus on improving DST module to generate dialogue states in circumstances with limited annotations and knowledge about slot ontology. To this end, we design a dual prompt learning framework for few-shot DST. The dual framework aims to explore how to utilize the language understanding and generation capabilities of pre-trained language models for DST efficiently. Specifically, we consider the learning of slot generation and value generation as dual tasks, and two kinds of prompts are designed based on this dual structure to incorporate task-related knowledge of these two tasks respectively. In this way, the DST task can be formulated as a language modeling task efficiently under few-shot settings. To evaluate the proposed framework, we conduct experiments on two task-oriented dialogue datasets. The results demonstrate that the proposed method not only outperforms existing state-of-the-art few-shot methods, but also can generate unseen slots. It indicates that DST-related knowledge can be probed from pre-trained language models and utilized to address low-resource DST efficiently with the help of prompt learning.

Fake news spread widely on social media in various domains, which lead to real-world threats in many aspects like politics, disasters, and finance. Most existing approaches focus on single-domain fake news detection (SFND), which leads to unsatisfying performance when these methods are applied to multi-domain fake news detection. As an emerging field, multi-domain fake news detection (MFND) is increasingly attracting attention. However, data distributions, such as word frequency and propagation patterns, vary from domain to domain, namely domain shift. Facing the challenge of serious domain shift, existing fake news detection techniques perform poorly for multi-domain scenarios. Therefore, it is demanding to design a specialized model for MFND. In this paper, we first design a benchmark of fake news dataset for MFDN with domain label annotated, namely Weibo21, which consists of 4,488 fake news and 4,640 real news from 9 different domains. We further propose an effective Multi-domain Fake News Detection Model (MDFEND) by utilizing domain gate to aggregate multiple representations extracted by a mixture of experts. The experiments show that MDFEND can significantly improve the performance of multi-domain fake news detection. Our dataset and code are available at https://github.com/kennqiang/MDFEND-Weibo21.

Retweeting is a powerful driving force in information propagation on microblogging sites. However, identifying the most effective retweeters of a message (called the ”key retweeter prediction” problem) has become a significant research topic. Conventional approaches have addressed this topic from two main aspects: by analyzing either the personal attributes of microblogging users or the structures of user graph networks. However, according to sociological findings, author-retweeter dependencies also play a crucial role in influencing message propagation. In this paper, we propose a novel model to solve the key retweeter prediction problem by incorporating the auxiliary relations between a tweet author and potential retweeters. Without loss of generality, we formulate the relations from four relational factors: status relation, temporal relation, locational relation, and interactive relation. In addition, we propose a novel method, called “Relation-based Learning to Rank (RL2R),” to determine the key retweeters for a given tweet by ranking the potential retweeters in terms of their spreadability. The experimental results show that our method outperforms the state-of-the-art algorithms at top-k retweeter prediction, achieving a significant relative average improvement of 19.7-29.4 percent. These findings provide new insights for understanding user behaviors on social media for key retweeter prediction purposes.

The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia content with images or videos to attract and mislead readers for rapid dissemination, which makes visual content an important part of fake news. Fake-news images, images attached to fake news posts, include not only fake images that are maliciously tampered but also real images that are wrongly used to represent irrelevant events. Hence, how to fully exploit the inherent characteristics of fake-news images is an important but challenging problem for fake news detection. In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively. Therefore, we propose a novel framework Multi-domain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news. Specifically, we design a CNN-based network to automatically capture the complex patterns of fake-news images in the frequency domain; and utilize a multi-branch CNN-RNN model to extract visual features from different semantic levels in the pixel domain. An attention mechanism is utilized to fuse the feature representations of frequency and pixel domains dynamically. Extensive experiments conducted on a real world dataset demonstrate that MVNN outperforms existing methods with at least 9.2% in accuracy, and can help improve the performance of multi-modal fake news detection by over 5.2%.

Microblogs have become one of the most popular platforms for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated on social networks, which could cause huge panic and threat during its propagation. In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts. Specifically, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM). We first build a hierarchical bidirectional long short-term memory model for representation learning. Then, the social contexts are incorporated into the network via attention mechanism, such that important semantic information is introduced to the framework for more robust rumor detection. Experimental results on two real world datasets demonstrate that the proposed method outperforms several state-of-the-arts in both rumor detection and early detection scenarios.

Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations from large-scale training data, and propose a novel scheme of Sparse Online Learning of Image Similarity (SOLIS). In contrast to many existing image-similarity learning algorithms that are designed to work with low-dimensional data, SOLIS is able to learn image similarity from large-scale image data in sparse and high-dimensional spaces. Our encouraging results showed that the proposed new technique achieves highly competitive accuracy as compared to the state-of-the-art approaches but enjoys significant advantages in computational efficiency, model sparsity, and retrieval scalability, making it more practical for real-world multimedia retrieval applications.

This demo presents an extremely efficient concept detection system based on a novel bag of words extraction method and sparse ensemble learning. We will show that the presented system can efficiently build the concept detectors upon millions of images, and achieve real-time concept detection on unseen images with the state-of-the-arts accuracy. To do so, we first develop an efficient bag of visual words (BoW) construction method based on sparse non-negative matrix factorization (NMF) and GPU enabled SIFT feature extraction. We then develop a sparse ensemble learning method to build the detection model, which drastically reduces learning time in order of magnitude over traditional methods like Support Vector Machine. The demo video of the system is available at YouTube: http://youtu.be/57obnlCxqAs

Conventional local detectors often extract numerous small repeated regions in textured areas, which easily results in false matching. In order to find representative and distinctive local invariant regions, this paper proposes a Maximally Visual-Homogeneous Region (MVHR) detector. The main contributions can be summarized as 2 parts: (1) Being different from original MSER which employs single pixel intensity as ranking unit, we propose a novel sorting method based on visual homogeneity analysis on a local patch. (2) Identifying the observation scale has a close relationship with visual homogeneity analysis, a heuristic scale selection algorithm is developed to choose a proper scale according to the changes of visual homogeneity evaluation over a range of scales. Experiments demonstrate our detector can find less but representative regions with high repeatability, while still perserving competitive precision compared to the state-of-art detectors for large scale image retrieval.

In Smart Grid applications, as the number of deployed electric smart meters increases, massive amounts of valuable meter data is generated and collected every day. To enable reliable data collection and make business decisions fast, high throughput storage and high-performance analysis of massive meter data become crucial for grid companies. Considering the advantage of high efficiency, fault tolerance, and price-performance of Hadoop and Hive systems, they are frequently deployed as underlying platform for big data processing. However, in real business use cases, these data analysis applications typically involve multidimensional range queries (MDRQ) as well as batch reading and statistics on the meter data. While Hive is high-performance at complex data batch reading and analysis, it lacks efficient indexing techniques for MDRQ. In this paper, we propose DGFIndex, an index structure for Hive that efficiently supports MDRQ for massive meter data. DGFIndex divides the data space into cubes using the grid file technique. Unlike the existing indexes in Hive, which stores all combinations of multiple dimensions, DGFIndex only stores the information of cubes. This leads to smaller index size and faster query processing. Furthermore, with pre-computing user-defined aggregations of each cube, DGFIndex only needs to access the boundary region for aggregation query. Our comprehensive experiments show that DGFIndex can save significant disk space in comparison with the existing indexes in Hive and the query performance with DGFIndex is 2-50 times faster than existing indexes in Hive and HadoopDB for aggregation query, 2-5 times faster than both for non-aggregation query, 2-75 times faster than scanning the whole table in different query selectivity.

In this article, we propose a unified geolocation framework to automatically determine where on the earth a web video was shot. We analyze different social, visual, and textual relationships from a real-world dataset and find four relationships with apparent geography clues that can be used for web video geolocation. Then, the geolocation process is formulated as an optimization problem that simultaneously takes the social, visual, and textual relationships into consideration. The optimization problem is solved by an iterative procedure, which can be interpreted as a propagation of the geography information among the web video social network. Extensive experiments on a real-world dataset clearly demonstrate the effectiveness of our proposed framework, with the geolocation accuracy higher than state-of-the-art approaches.

State-of-the-art image search systems mostly build on bag-of-features (BOF) representation. As BOF ignores geometric relationships among local features, geometric consistency constraints have been proposed to improve search precision. However, exploiting full geometric constraints are too computational expensive. Weak geometric constraints have strong assumptions and can only deal with uniform transformations. To handle view point changes and nonrigid deformations, in this paper we present a novel pairwise weak geometric consistency constraint (P-WGC) method. It utilizes the local similarity characteristic of deformations, and measures the pairwise geometric similarity of matches between two sets of local features. Experiments performed on four famous datasets and a dataset of one million of images show a significant improvement due to P-WGC as well as its efficiency. Further improvement of search accuracy is obtained when it is combined with full geometric verification.

Most existing query expansion approaches for ad-hoc retrieval adopt overly simplistic textual representations that treat documents as bags of words and ignore inherent document structure. These simple representations often lead to incorrect independence assumptions in the proposed approaches and result in limited retrieval effectiveness. In this paper, we propose a novel query expansion technique that models the various types of dependencies that exist between original query terms and expansion terms within a robust, unified framework. The proposed model is called Hierarchical Markov random fields (HMRFs), based on Latent Concept Expansion (LCE). By exploiting implicit (or explicit) hierarchical structure within documents, HMRFs can incorporate hierarchical interactions which are important for modeling term dependencies in an efficient manner. Our rigorous experimental evaluation carried out using several TREC data sets shows that our proposed query expansion technique consistently and significantly outperforms the current state-of-the-art query expansion approaches, including relevance-based language models and LCE.

Tag recommendation is a common way to enrich the textual annotation of multimedia contents. However, state-of-the-art recommendation methods are built upon the pair-wised tag relevance, which hardly capture the context of the web video, i.e., when who are doing what at where. In this paper we propose the context-oriented tag recommendation (CtextR) approach, which expands tags for web videos under the context-consistent constraint. Given a web video, CtextR first collects the multi-form WWW resources describing the same event with the video, which produce an informative and consistent context; and then, the tag recommendation is conducted based on the obtained context. Experiments on an 80,031 web video collection show CtextR recommends various relevant tags to web videos. Moreover, the enriched tags improve the performance of web video categorization.

Generating similar queries for a query, named query suggestion, is an important technology for helping search engine users. Since query data is very diverse and sparse, it is still challenging to measure the similarity of each query pair. We propose a novel algorithm called QueryTrans, which can efficiently compute pairwise similarity scores between all queries with respect to the global structure of a query trace graph mined from search engine logs. Compared with previous query suggestion approaches, QueryTrans is robust for different queries and stable for different parameter settings. We also present the performance of QueryTrans on large scale query logs. Experiments on about 100,000 queries show: QueryTrans can efficiently computes almost 10 billion pairwise similarity scores within 15 minutes on a single computer; and its results are significantly better than all 4 recent approaches on query suggestion.

In this paper, we highlight the use of multimedia technology in generating intrinsic summaries of tourism related information. The system utilizes an automated process to gather, filter and classify information on various tourist spots on the Web. The end result present to the user is a personalized multimedia summary generated with respect to users queries filled with text, image, video and real-time news made retrievable for mobile devices. Preliminary experiments demonstrate the superiority of our presentation scheme to traditional methods.