Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, such as writing patterns that include emotional words, stylistic features, etc. However, detectors tuned solely to such semantic clues can easily fall into surface detection patterns, which can shift rapidly in dynamic environments, leading to limited performance in the evolving news landscape. To address this issue, this paper investigates a novel perspective by incorporating news intent into fake news detection, bridging intents and semantics together. The core insight is that by considering news intents, one can deeply understand the inherent thoughts behind news deception, rather than the surface patterns within words alone. To achieve this goal, we propose Graph-based INtent-Semantic joInt moDEling (InSide) for fake news detection, which models deception clues from both semantic and intent signals via graph-based joint learning. Specifically, Inside reformulates news semantic and intent signals into heterogeneous graph structures, enabling long-range context interaction through entity guidance and capturing both holistic and implementation-level intent via coarse-to-fine intent modeling. To achieve better alignment between semantics and intents, we further develop a dynamic pathway-based graph alignment strategy for effective message passing and aggregation across these signals by establishing a common space. Extensive experiments on four benchmark datasets demonstrate the superiority of the proposed Inside compared to state-of-the-art methods.

Online fake news moderation now faces a new challenge brought by the malicious use of large language models (LLMs) in fake news production. Though existing works have shown LLM-generated fake news is hard to detect from an individual aspect, it remains underexplored how its large-scale release will impact the news ecosystem. In this study, we develop a simulation pipeline and a dataset with ~56k generated news of diverse types to investigate the effects of LLM-generated fake news within neural news recommendation systems. Our findings expose a truth decay phenomenon, where real news is gradually losing its advantageous position in news ranking against fake news as LLM-generated news is involved in news recommendation. We further provide an explanation about why truth decay occurs from a familiarity perspective and show the positive correlation between perplexity and news ranking. Finally, we discuss the threats of LLM-generated fake news and provide possible countermeasures. We urge stakeholders to address this emerging challenge to preserve the integrity of news ecosystems.

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.

The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the future. Though great efforts have been made, existing fake news detection methods overlooked the unintended entity bias in the real-world data, which seriously influences models' generalization ability to future data. For example, 97% of news pieces in 2010-2017 containing the entity 'Donald Trump' are real in our data, but the percentage falls down to merely 33% in 2018. This would lead the model trained on the former set to hardly generalize to the latter, as it tends to predict news pieces about 'Donald Trump' as real for lower training loss. In this paper, we propose an entity debiasing framework (ENDEF) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective. Based on the causal graph among entities, news contents, and news veracity, we separately model the contribution of each cause (entities and contents) during training. In the inference stage, we remove the direct effect of the entities to mitigate entity bias. Extensive offline experiments on the English and Chinese datasets demonstrate that the proposed framework can largely improve the performance of base fake news detectors, and online tests verify its superiority in practice. To the best of our knowledge, this is the first work to explicitly improve the generalization ability of fake news detection models to the future data. The code has been released at https://github.com/ICTMCG/ENDEF-SIGIR2022.

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.