Towards Self-Supervised Cross-Domain Fake News Detection

Twitter, Facebook, and Instagram are just some examples of social media currently used by people to share news with other users worldwide. However, the information widespread through these channels is typically unverified and/or interpreted according to the user's point of view. Accordingly, those means represent the perfect tool to hack user opinions with misleading or false news and make fake news viral. Identifying this malicious information is a crucial but challenging task since fake news can concern different topics. Indeed, the detection models learned against a specific domain will exhibit poor performances when tested on a different one. In this work, we propose a novel deep learning-based architecture able to mitigate this problem by yielding cross-domain high-level features for addressing this task. Preliminary experimentation conducted on two benchmarks demonstrated the validity of the proposed solution.