Memory-Guided Multi-View Multi-Domain Fake News Detection

Abstract

The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of multiple domains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) domain shift, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) domain labeling incompleteness, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M${}^3$FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains. Extensive offline experiments on English and Chinese datasets demonstrate the effectiveness of M${}^3$FEND, and online tests verify its superiority in practice. Our code is available at https://github.com/ICTMCG/M3FEND.

Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)

References in corpus (14)

• Fake News Detection on Social Media: A Data Mining Perspective
• Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
• Contrastive Multi-View Representation Learning on Graphs
• Multi-Representation Adaptation Network for Cross-domain Image Classification
• MDFEND: Multi-domain Fake News Detection
• ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
• Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues
• Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
• Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
• User-Centric Conversational Recommendation with Multi-Aspect User Modeling
• Multimodal Emergent Fake News Detection via Meta Neural Process Networks
• Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning
• MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
• Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims