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

Yongchun Zhu

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 necessary 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 (M3FEND) 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 M3FEND, and online tests verify its superiority
in practice. Our code is available at https://github.com/ICTMCG/M3FEND.