External information-augmented contrastive learning framework for fake news detection

The proliferation of fake news and information overload on social media has led to increased public confusion and poses a serious threat to social stability. Traditional fake news detection methods typically focus solely on the content of the news itself, making them vulnerable to manipulation by disinformation campaigns. This limitation highlights the need for a more comprehensive approach that incorporates external information to improve detection accuracy. In response to this challenge, we propose a novel framework for fake news detection, named External Information-Augmented Contrastive Learning (EACL). The EACL framework consists of three key modules: (1) the External Information Construction Module, which utilizes entity linking, embedding, and retrieval techniques to analyze news from both factual and public opinion perspectives, thus creating an analysis-friendly environment; (2) the Consistency Feature Extraction Module, which employs a distance-aware signed attention mechanism to model the consistency between news content and external information, while filtering out irrelevant data; and (3) the Comparative Learning Enhancement Module, which constructs positive and negative sample pairs to enhance the learning of semantic differences between fake and real news. Extensive qualitative and quantitative experiments conducted on two real-world datasets demonstrate that EACL achieves impressive accuracy rates of 85.2% and 82.9%, significantly outperforming existing baseline methods. The results further illustrate the effectiveness of integrating external information and contrastive learning in combating misinformation.