The prevalence of social media platforms has accelerated the propagation of fake news, with carefully crafted multimodal fake news often gaining public trust and causing severe impacts. Methods based on multimodal small language models (MSLMs) struggle in scenarios with scarce labeled data. Large vision-language model (LVLM)-based approaches, though alleviating this limitation, often face two major problems: They rely too much on text-image consistency when predicting the authenticity of news. They judge which samples need to introduce external knowledge based on LVLMs’ direct outputs, which is not accurate because of LVLMs’ overconfidence. This article proposes a novel LVLM-based multiview multimodal fake news detection (MV-MFND) framework to address these problems. MV-MFND leverages LVLMs to analyze text, image, and text-image pairs separately, thus predicting authenticity from multiple perspectives. MV-MFND determines which samples need to introduce external knowledge by analyzing the softmax probability of specific tokens output by the LVLM. Our framework MV-MFND achieves SOTA performance on three real-world datasets Fakeddit, Twitter, and MR2-en on Qwen.

Knowledge-aware Recommendation (KGR) aims to utilize a knowledge graph to provide rich side information for items in a recommendation system and construct a unified graph containing users, items, and entities. In this paper, we present a new graph neural network for user-item-entity interaction modeling, named Graph Attention Intent Network, it employs different strategies to aggregate user and item information to generate high-quality representations. Typically, the description of user-item interactions is modeled as a bipartite graph, which overlooks the relations between users and between items, a significant aspect of realistic recommendation. Therefore, we propose a framework, named knowledge-aware multi-view contrastive learning for recommendation. It can explore effective user-user and item-item relations in the heterogeneous network of KGR, construct a user social graph and an item similarity graph, and combine the information of the two views into user-item-entity interaction modeling to enhance the representation of users and items. We introduce cross-graph contrastive learning to facilitate the integration of heterogeneous information while alleviating the sparse labeling problem of recommendation tasks. Experimental results on three benchmark datasets show that our model is more effective than other state-of-the-art models.

From the perspective of philosophy, ontology relations denote ultimate semantic relations of related knowledge concepts. Beyond doubt, it is still a very difficult problem on how to automatically depict and construct ontology relations because of its high abstractness. Some latest research attempted to realize ontology relation learning by learning abstract hierarchies or similarities among knowledge concepts. Inspired by the requirements of associative semantic cognition like in the human brain, a constructivist ontology relation learning (CORL) method is put forward in this study by borrowing the idea of the constructivist learning theory. Wherein, two following points are supposed: 1) each symbol knowledge is looked as a token of representing certain abstract pattern and 2) each pattern denotes a type of relation structures on other patterns, or a directly observed event data, such as physical sensing data, natural image, sound data, text word etc. So, ontology relation could be considered as the associative support degrees from other knowledge concepts to the target concept, which reflects how one knowledge ontology can be demarcated by other knowledge concepts. Then, the knowledge network can be employed to represent an entire domain knowledge system. Meanwhile, an associative random walk mechanism (ARWM) on knowledge network can be considered to explain the semantic generative process of every document. Thus, CORL can be realized by integrating ARWM into an extended latent Dirichlet allocation (LDA) model. Some theoretical and experimental analysis are done. The corresponding results demonstrate that CORL can obtain effective associative semantic relations among concept words, and gain some novel characteristics in better representing knowledge ontology than existing methods.

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendat ion and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification.

E-cigarettes(vape) are now the most commonly used tobacco product among youth in the United States. Ads are claiming e-cigarettes help smokers quit, but most of them contain nicotine, which can cause addiction and harm the developing adolescent brain. Therefore national, state and local health organizations have proposed anti-vaping campaigns to warn the potential risks of e-cigarettes. Since there is not definitive evidence that e-cigarettes cause long-term harm, these campaigns received pro-vapors' fight back, and collected a high volume of opponent messages in social media. Thus when we analyze the feedback of anti-vaping campaigns, it is crucial to partition audience into different clusters according to their attitude. Motivated by this, in this paper, we propose the “Community Detection on Anti-vaping Campaign Audience (CodeVan)” problem and design the Sorento method to solve it. Sorento computes users' intimacy scores based on their social connections, repost relations and content similarities. Extensive experiments show the effectiveness of the Sorento algorithm.

Traditional viral marketing problems aim at selecting a subset of seed users for one single product to maximize its awareness in social networks. However, in real scenarios, multiple products can be promoted in social networks at the same time. At the product level, the relationships among these products can be quite intertwined, e.g., competing, complementary and independent. In this paper, we will study the “interTwined Influence Maximization” (i.e., TIM) problem for one product that we target on in online social networks, where multiple other competing/complementary/independent products are being promoted simultaneously. The TIM problem is very challenging to solve due to (1) few existing models can handle the intertwined diffusion procedure of multiple products concurrently, and (2) optimal seed user selection for the target product may depend on other products' marketing strategies a lot. To address the TIM problem, a unified greedy framework TIER (interTwined Influence EstimatoR) is proposed in this paper. Extensive experiments conducted on four different types of real-world social networks demonstrate that TIER can outperform all the comparison methods with significant advantages in solving the TIM problem.

Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved campaigns will produce their changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Anti-smoking television advertising is the most important part in the campaign. Meanwhile nowadays successful online social networks are creating new media environment, however little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the “Social Influence inference of anti-Tobacco mass mEdia campaigns” (SITE) problem. To address the SITE problem, a novel influence inference framework, “TV Advertising Social Influence Estimation” (ASIE), is proposed based on our analysis of two anti-tobacco campaigns. ASIE divides audience attitudes towards TV ads into three distinct stages: (1) Cognitive, (2) Affective and (3) Conative. Audience online reactions at each of these three stages are depicted by ASIE with specific probabilistic models based on the synergistic influences from both online social friends and offline TV ads. Extensive experiments demonstrate the effectiveness of ASIE.

People nowadays need to spend a large amount of time on their work everyday and workplace has become an important social occasion for effective communication and information exchange among employees. Besides traditional online contacts (e.g., face-to-face meetings and telephone calls), to facilitate the communication and cooperation among employees, a new type of online social networks has been launched inside the firewalls of many companies, which are named as the "enterprise social networks" (ESNs). In this paper, we want to study the information diffusion among employees at workplace via both online ESNs and online contacts. This is formally defined as the IDE (Information Diffusion in Enterprise) problem. Several challenges need to be addressed in solving the IDE problem: (1) diffusion channel extraction from online ESN and online contacts; (2) effective aggregation of the information delivered via different diffusion channels; and (3) communication channel weighting and selection. A novel information diffusion model, Muse (Multi-source Multi-channel Multi-topic diffUsion SElection), is introduced in this paper to resolve these challenges. Extensive experiments conducted on real-world ESN and organizational chart dataset demonstrate the outstanding performance of Muse in addressing the IDE problem.

As more and more people join social network, viral marketing on online social network becomes a new trend of advertising. Motivated by this, plenty of research focuseson how to maximize the information propagation, which is called the influence maximization problem. Traditional work has made significant progress on this topic. However all ad companies have marketing budget, the research of influence maximization problem should take account of cost control. Under the condition of cost control, we model each user's cost of helping spread information as a feature of each node in the network. Then we modify several most widely studied algorithms to suit the new model. In this paper, a new algorithm called CPP-SNS is proposed, which selects seeds according to cost performance of nodes. Further improvements, based on strategy of partial node loading and submodular property of spread function, make CPP-SNS more effective in practical scenarios. Extensive experiments show this method has a good performance in different social networks. Based on results of our research, we also provide some advice for the practical marketing.