MDFEND: Multi-domain Fake News Detection

Abstract

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 https://github.com/kennqiang/MDFEND-Weibo21.

CIKM 2021 short paper. 5 pages

References in corpus (4)

• Distributed Representations of Sentences and Documents
• Fake News Detection on Social Media: A Data Mining Perspective
• Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
• Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

Cited by in corpus (2)

• Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
• Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning