MESE: Mining Emotional and Semantic Evolution From User Comments for Fake News Detection

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.