Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection (2024)

Abstract: The swift detection of multimedia fake news has emerged as a crucial task in combating malicious propaganda and safeguarding the security of the online environment. While existing methods have achieved commendable results in modeling entity-level inconsistency, addressing event-level inconsistency following the inherent subject-predicate logic of news and robustly learning news representations from poor-quality news samples remain two challenges. In this paper, we propose an Event-diven fake news detection framework (Event-Radar) based on multi-view learning, which integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news detection. Specifically, leveraging the capability of graph structures to capture interactions between events and parameters, Event-Radar captures event-level multimodal inconsistency by constructing an event graph that includes multimodal entity subject-predicate logic. Additionally, to mitigate the interference of poor-quality news, Event-Radar introduces a multi-view fusion mechanism, learning comprehensive and robust representations by computing the credibility of each view as a clue, thereby detecting fake news. Extensive experiments demonstrate that Event-Radar achieves outstanding performance on three large-scale fake news detection benchmarks. Our studies also confirm that Event-Radar exhibits strong robustness, providing a paradigm for detecting fake news from noisy news samples.

## introduction against the backdrop of the rapid expansion of social media, online platforms like twitter have emerged as the primary channels for people to obtain information. unfortunately, they have also the police arrested a protester. protesters confronted the police. ## methodology fig. 2 illustrates an overview of the event-radar framework, comprising a multi-view modeling layer and a credibility estimation layer. specifically, we initially encode the events, emotions, and pattern information of the news to comprehensively assess the news representation from various views. to model multimodal news events, we introduce an event inconsistency measurement module based on event subgraphs. to obtain credible representations from each view, we employ beta distribution to compute the credibility of each view and fuse modality features guided by this credibility. subsequently, for information interaction, we employ a self-attention mechanism to fuse modal information from various views. finally, we employ a classifier to perform fake news detection. ## conclusion event-radar is a novel fake news detection framework that demonstrates exceptional event modeling capabilities and significant robustness, aimed at addressing the issue of maliciously crafted fake news. extensive experiments validate that event-radar's classification performance surpasses all listed strong benchmarks. further research confirms the effectiveness of our initial technical contributions, emphasizing event-radar's ability to resist interference from poor-quality news.