ExFake: Towards an Explainable Fake News Detection Based on Content and Social Context Information
This addresses the problem of fake news spread on social networks for users and platforms, but it appears incremental as it builds on existing detection methods by adding explainability and user credibility scoring.
The authors tackled fake news detection by developing ExFake, a system that uses content, social context, and trusted data sources, and includes an explainable AI assistant to help users; it significantly outperformed baseline methods in experiments on a real-world dataset.
ExFake is an explainable fake news detection system based on content and context-level information. It is concerned with the veracity analysis of online posts based on their content, social context (i.e., online users' credibility and historical behaviour), and data coming from trusted entities such as fact-checking websites and named entities. Unlike state-of-the-art systems, an Explainable AI (XAI) assistant is also adopted to help online social networks (OSN) users develop good reflexes when faced with any doubted information that spreads on social networks. The trustworthiness of OSN users is also addressed by assigning a credibility score to OSN users, as OSN users are one of the main culprits for spreading fake news. Experimental analysis on a real-world dataset demonstrates that ExFake significantly outperforms other baseline methods for fake news detection.