A hierarchical dual-view model for fake news detection guided by discriminative lexicons

Fake news detection aims to automatically identify the credibility of source posts, mitigating potential societal harm and conserving human resources. Textual fake news detection methods can be categorized into pattern- and fact-based. Pattern-based models focus on identifying shared writing patterns in source posts, while fact-based models leverage auxiliary external knowledge. Researchers have recently attempted to merge these two views into a comprehensive detection system, achieving superior performance to single-view methods. However, existing dual-view methods often prioritize integrating single-view methods over exploring nuanced characteristics of both perspectives. To address this, we propose a novel hierarchical dual-view model for fake news detection guided by discriminative lexicons. First, we construct two lexicons based on distinct word usage tendencies in fake and real news and further augment them with synonyms sourced from large language models. We then devise a hierarchical attention network to derive semantic representations for the source post, incorporating a lexicon attention loss to guide the prioritization of words from the two lexicons. Subsequently, a lexicon-guided interaction network is employed to model the relations between the source post and its relevant articles, assigning authenticity-aware weights to each article. Finally, the representations of source post and relevant articles are concatenated for joint detection. According to experimental results, our model outperforms many competitive baselines in terms of the macro F1 score ranging from 1.1% to 10.5% on Weibo and from 3.2% to 10.8% on Twitter.