Fine-Grained Discrepancy Contrastive Learning for Robust Fake News Detection

In recent years, fake news on social media has become a significant threat to societal security, elevating fake news detection to a research priority. Among various strategies, fact-checking detection methods stand out for their accuracy, leveraging evidence from dedicated fact databases. However, these methods often retrieve raw truth, including vast amounts of irrelevant data, based on semantic similarity. This approach results in information redundancy and risks missing the nuanced differences between fake news and the truth. As a result, subtle changes in fake news can greatly increase the risk of misclassification, compromising the methods’ robustness. To this end, we propose a robust fake news detection framework with Fine-grained Discrepancy Contrastive Learning (FinDCL). By simulating subtle discrepancies between fake news and event-related truth, our method enhances the capture and identification of nuanced falsehoods. Specifically, we construct an adversarial dataset to pre-train a fine-grained discrepancy calculation module with contrastive learning. Moreover, the truth extraction module is devised to alleviate information redundancy by extracting event-related truth. At last, FinDCL jointly utilizes the aforementioned modules to detect fake news in event truth-known and truth-unknown scenarios. Extensive experiments on six real-world datasets demonstrate the effectiveness of FinDCL.