Misinformation detection with automatic fact-based news verification

Applying fact-based verification to online misinformation detection can be a complex task due to the challenges of varying input lengths and the lack of sufficient data to train fact-based verification models for misinformation detection. We collected and created two specific datasets consisting of news articles and a set of fact articles (fact pool) which are used to develop a novel online misinformation detection pipeline based on information retrieval and claim verification. In addition to the AveriTec dataset, we collected and published the Misbar 1 1 https://anonymous.4open.science/r/Misinformation-detection-using-automatic-fact-based-news-verification-65D3 . dataset, which consists of expert-annotated long news articles and verification articles. We also propose a novel eXplainable Misinformation Detection method with Fact Verification, called XMDFaVer, that detects online misinformation through several stages: text summarization to condense lengthy news articles into short claims, a question generation and answering model to clarify the claim with reference to the associated fact pool, and finally a classifier to assess the claim against the factual data and discern the authenticity of the news articles. XMDFaVer demonstrates strong and consistent performance across most test cases, outperforming standard and fact-based baselines. The results also show that using more fact articles improves detection accuracy, confirming the effectiveness and robustness of the approach.