Multi-Agent Cross-Lingual Veracity Assessment for Explainable Fake News Detection

AbstractThe spread of fake news during the COVID-19 pandemic era triggered widespread chaos and confusion globally, causing public panic and misdirected health behavior. Automated fact checking in non-English languages is challenging due to the low availability of trusted resources. There are several prior work that attempted automated fact checking in multilingual settings. However, most of them fine-tune pre-trained language models (PLMs) and only produce veracity prediction without providing explanations. The absence of explanatory reasoning in these models reduces the credibility of their predictions. This paper proposes a multi-agent explainable cross-lingual fake news detection method that leverages credible English evidence and Large Language Models (LLMs) to verify and generate explanations for non-English claims, overcoming the scarcity of non-English evidence. The experimental results show that the proposed method performs well across three non-English written multilingual COVID-19 datasets in terms of veracity predictions and explanations. Our source code is available online. (https://github.com/bassamtiano/crosslingual_efnd)