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) <bold/>domain shift<bold/>, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) <bold/>domain labeling incompleteness<bold/>, 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 <inline-formula><tex-math notation="LaTeX">$