SARC: Sentiment-Augmented Deep Role Clustering for Fake News Detection

TL;DR

SARC introduces a sentiment-augmented deep role clustering framework that leverages user role differentiation and joint optimization to enhance fake news detection accuracy on social media datasets.

Contribution

The paper presents a novel deep clustering approach that incorporates sentiment and role differentiation for improved fake news detection.

Findings

SARC outperforms baseline models on RumourEval-19 and Weibo-comp datasets.

Joint role clustering and fake news detection improve overall accuracy.

Sentiment and role-aware features enhance detection performance.

Abstract

Fake news detection has been a long-standing research focus in social networks. Recent studies suggest that incorporating sentiment information from both news content and user comments can enhance detection performance. However, existing approaches typically treat sentiment features as auxiliary signals, overlooking role differentiation, that is, the same sentiment polarity may originate from users with distinct roles, thereby limiting their ability to capture nuanced patterns for effective detection. To address this issue, we propose SARC, a Sentiment-Augmented Role Clustering framework which utilizes sentiment-enhanced deep clustering to identify user roles for improved fake news detection. The framework first generates user features through joint comment text representation (with BiGRU and Attention mechanism) and sentiment encoding. It then constructs a differentiable deep clustering module to automatically categorize user roles. Finally, unlike existing approaches which take fake news label as the unique supervision signal, we propose a joint optimization objective integrating role clustering and fake news detection to further improve the model performance. Experimental results on two benchmark datasets, RumourEval-19 and Weibo-comp, demonstrate that SARC achieves superior performance across all metrics compared to baseline models. The code is available at: https://github.com/jxshang/SARC.