Fake news spread widely on social media in various domains, which lead to
real-world threats in many aspects like politics, disasters, and finance. Most
existing approaches focus on single-domain fake news detection (SFND), which
leads to unsatisfying performance when these methods are applied to
multi-domain fake news detection. As an emerging field, multi-domain fake news
detection (MFND) is increasingly attracting attention. However, data
distributions, such as word frequency and propagation patterns, vary from
domain to domain, namely domain shift. Facing the challenge of serious domain
shift, existing fake news detection techniques perform poorly for multi-domain
scenarios. Therefore, it is demanding to design a specialized model for MFND.
In this paper, we first design a benchmark of fake news dataset for MFND with
domain label annotated, namely Weibo21, which consists of 4,488 fake news and
4,640 real news from 9 different domains. We further propose an effective
Multi-domain Fake News Detection Model (MDFEND) by utilizing a domain gate to
aggregate multiple representations extracted by a mixture of experts. The
experiments show that MDFEND can significantly improve the performance of
multi-domain fake news detection. Our dataset and code are available at
this https URL.

本文针对多领域假新闻检测模型的数据分布及域偏移的挑战，在一个包含 9 个不同领域的测评数据集 Weibo21 上，使用多个领域表征进行融合的领域门控模型 MDFEND，显著提高了多领域假新闻检测性能。