Fake news detection is a hot topic in the social media mining research community. Recent studies have shown that sentiment signals could significantly benefit the detection performance. However, most existing methods treat sentiment merely as auxiliary features, while the more sophisticated social sentiment interactions were rarely explored. In this paper, we propose a novel framework named ReFEND, which leverages the sentiment resonances among the social users (i.e., social sentiment resonances) and the sentiment relationship between news content and user comments to improve the detection performance. Specifically, we first utilize sentiment scorers to assess the sentiment of comments and identify users' emotional tendencies. Then we creatively construct a sentiment-aware multi-relational graph to capture social sentiment resonances evoked by the content and the interactions between comments and news. Next, we leverage the relational graph convolutional network (RGCN), which specializes in handling multi-relational graph data, to learn the interactions on sentiment-aware graph. To our best knowledge, this is the first effort to leverage social sentiment resonances for fake news detection. Experimental results on three datasets indicate that ReFEND significantly outperforms the state-of-the-art sentiment-based methods in terms of F1 and accuracy. Besides, ablation studies demonstrate the effectiveness of components designed in ReFEND.

Nowadays, social media platforms have become primary channels for dissemination of fake news. On these platforms, user comments provide direct reactions and insights into the content being shared, offering valuable clues for effective fake news detection. However, existing approaches predominantly analyze comments from an isolated, single-comment perspective, overlooking the broader insights from the entire comment section. To address this limitation, this paper comprehensively considers three key factors within the comment section: emotional evolution, semantic evolution, and diversity of user attention, based on which a novel fake news detection model MESE is proposed by mining the emotional and semantic evolution from user comments. Specifically, to capture the diversity of user attention toward different news segments, we first propose a news-conditioned comment attention mechanism to obtain news-enhanced comment representations. Next, a gating mechanism is introduced to deeply integrate emotional and semantic features. Additionally, we develop a comment emotional and semantic evolution module to capture shifts in public reactions over time. Finally, these diverse representations are fused to generate prediction results. Extensive experiments on two public datasets demonstrate the superior performance of MESE. Further case studies and ablation experiments validate the rationality of our design and the effectiveness of the model components.