MDFEND: Multi-domain Fake News Detection

TL;DR

This paper introduces MDFEND, a novel model for multi-domain fake news detection, and provides a new benchmark dataset, Weibo21, to address the challenges of domain shift in fake news classification across various fields.

Contribution

The paper presents a new multi-domain fake news dataset and a specialized model with a domain gate to improve detection performance across diverse domains.

Findings

MDFEND significantly outperforms existing methods in multi-domain fake news detection.

The Weibo21 dataset contains 9 domains with annotated fake and real news.

The domain gate effectively handles domain shift in 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.