With the rise of generative AI, the boundary between authentic and deceptive content has become increasingly ambiguous, challenging traditional fake news detection methods that rely solely on observable content or propagation structures. These approaches often neglect the underlying psychological motivations driving user behavior, leaving them susceptible to adversarial manipulation. However, as the user decision-making process is inherently unobservable, conventional deep learning models struggle to capture the cognitive mechanisms behind information sharing. To address this, we propose TPB-VAE, a psychologically grounded framework that integrates the Theory of Planned Behavior (TPB) with large language models (LLMs) to infer and encode users' latent intent. TPB-VAE maps TPB constructs into a latent space, making the decision-making process computationally accessible. It employs semi-supervised learning specifically to infer users' latent intent from a small subset of labeled samples, and uses the resulting intents to derive rich behavioral features for more reliable fake news detection. Extensive experiments on four real-world datasets demonstrate the effectiveness and adversarial resilience of our approach.

Recently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, which is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identities, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. Specifically, we innovatively design a Hypergraph-based Global interaction learning module to capture the global preferences of users from their co-spreading relationships, and introduce node centrality encoding to complement user influence in hypergraph learning. Moreover, the designed Self-attention-based Local context learning module first introduce spread status to highlight the propagation ability of news and users, thus providing additional signals for verifying news authenticity. Experiments on real-world datasets indicate that our HG-SL, which solely relies on user behavior, outperforms SOTA baselines utilizing multidimensional features in both fake news detection and early detection task.

Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture user' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memory-enhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.

The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analysed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense advancements in this field urged the researchers to devise new techniques and approaches, each sermonizing a different research analysis/question, that cope with upcoming issues and complex scenarios of Aspect-based Sentiment Analysis. Therefore, this survey emphasized on the issues and challenges that are related to extraction of different aspects and their relevant sentiments, relational mapping between aspects, interactions, dependencies, and contextual-semantic relationships between different data objects for improved sentiment accuracy, and prediction of sentiment evolution dynamicity. A rigorous overview of the recent progress is summarized based on whether they contributed towards highlighting and mitigating the issue of Aspect Extraction, Aspect Sentiment Analysis or Sentiment Evolution. The reported performance for each scrutinized study of Aspect Extraction and Aspect Sentiment Analysis is also given, showing the quantitative evaluation of the proposed approach. Future research directions are proposed and discussed, by critically analysing the presented recent solutions, that will be helpful for researchers and beneficial for improving sentiment classification at aspect-level.

One of the interesting trending phenomena in sentiment analysis is the prediction of sentiment given by the user towards an aspect term. Till today, a considerable number of researchers have proposed varying methodologies for predicting aspect-based sentiments. But they mostly encapsulate the semantic information by manifesting themselves within a local boundary around each aspect term and overlook capturing the semantic concept that is conveyed within the entire review (global). Therefore, this study proposes a model,IAF-LG, that performs semantic learning at both local and global scales to discover aspect-based sentiments. IAF-LG first encodes the local semantics by fusing contextual-semantic dependencies between tokens and computing relational semantics between inter-aspects. Next, it develops the global semantics by formulating interactions between local semantics and review-based sentiment learning. Lastly, it conjoins the local and global interactive learning to earn credible semantics for predicting the accurate sentiment of aspect terms. Extensive experiments on publicly available datasets demonstrate the significantly improved performance of IAF-LG than competitive baselines.

Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these conflicts are not necessarily all about questioning the false part of claims, which makes considerable semantic conflicts difficult to be used as evidence to explain the results of claim verification. In this paper, we propose evidence inference networks (EVIN), which focus on the conflicts questioning the core semantics of claims and serve as evidence for interpretable claim verification. Specifically, EVIN first captures the core semantic segments of claims and the users' principal opinions in relevant articles. Then, it finely-grained identifies the semantic conflicts contained in each relevant article from these opinions. Finally, it constructs coherence modeling to match the conflicts that queries the core semantic fragments of claims as explainable evidence. Experiments on two widely used datasets demonstrate that EVIN not only achieves satisfactory performance but also provides explainable evidence for end-users.

Lianwei Wu, Yuan Rao, Yuqian Lan, Ling Sun, Zhaoyin Qi. 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.

Lianwei Wu, Yuan Rao, Haolin Jin, Ambreen Nazir, Ling Sun. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.