Perspective Collaboration for Multi-domain Fake News Detection

Abstract

Fake news is widely spread on social media. Much research works have been done on automatic fake news detection in single domain. However, fake news exists in various domains, so the detection model based on single domain is less effective in multiple domain scenes. To improve the detection ability of multi-domain fake news, we propose a perspective collaboration for multi-domain fake news detection (PCMFND) method to detect fake news across multiple domains by combining the powerful feature extraction ability of expert systems. The method extracts features of different perspectives from news content separately, then interactively combines the features of different perspectives, and ultimately achieves fake news detection by adaptively aggregating features of each perspective through domain knowledge. The effectiveness of the proposed method is demonstrated through comparison experiments with traditional multi-domain detection methods on Chinese and English multi-domain datasets.