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In recent years, we witness the explosion of false and unconfirmedinformation (i.e., rumors) that went viral on social media and shocked thepublic. Rumors can trigger versatile, mostly controversial stance expressionsamong social media users. Rumor verification and stance detection are differentyet relevant tasks. Fake news debunking primarily focuses on determining thetruthfulness of news articles, which oversimplifies the issue as fake newsoften combines elements of both truth and falsehood. Thus, it becomes crucialto identify specific instances of misinformation within the articles. In thisresearch, we investigate a novel task in the field of fake news debunking,which involves detecting sentence-level misinformation. One of the majorchallenges in this task is the absence of a training dataset withsentence-level annotations regarding veracity. Inspired by the MultipleInstance Learning (MIL) approach, we propose a model called Weakly SupervisedDetection of Misinforming Sentences (WSDMS). This model only requires bag-levellabels for training but is capable of inferring both sentence-levelmisinformation and article-level veracity, aided by relevant social mediaconversations that are attentively contextualized with news sentences. Weevaluate WSDMS on three real-world benchmarks and demonstrate that itoutperforms existing state-of-the-art baselines in debunking fake news at boththe sentence and article levels.

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom