Discovering Domain-Agnostic Fake News Detectors Through Deep Self-Supervised Learning

Abstract

The rapid spread of misinformation across online platforms poses a major threat to societal trust, public health, and democratic processes. While recent advances in machine learning have improved the accuracy of fake news detection, most existing approaches remain limited to single-domain settings and struggle to generalize across diverse domains or platforms. To address this challenge, we propose DAFNE (Domain-Agnostic Fake NEws detector), a deep learning approach designed to capture cross-domain high-level features for fake news detection. By combining feature-level adversarial learning with self-supervised learning, DAFNE effectively learns domain-invariant representations that enable reliable detection across heterogeneous sources. The proposed approach is evaluated on five real-world benchmark datasets spanning multiple domains, and the results demonstrate superior generalization capabilities compared to state-of-the-art baselines. Specifically, DAFNE outperforms the competitors, with average micro-F1 improvements ranging from 11.3% to 39.9%. In comparison to the second-best model, our approach shows an average improvement of 18% across all domains in terms of the F-Score, reaching up to 25% on the Politifact dataset. These results highlight the capability of DAFNE to mitigate the domain shift problem, enabling more reliable and adaptive misinformation detection in dynamic online environments.