  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.
