Robust Misinformation Detection by Visiting Potential Commonsense Conflict

• URL: http://arxiv.org/abs/2504.21604v1
• Date: Wed, 30 Apr 2025 13:03:17 GMT
• Title: Robust Misinformation Detection by Visiting Potential Commonsense Conflict
• Authors: Bing Wang, Ximing Li, Changchun Li, Bingrui Zhao, Bo Fu, Renchu Guan, Shengsheng Wang,
• Abstract summary: Misinformation Detection (MD) aims to detect online misinformation automatically.<n>We propose a novel plug-and-play augmentation method for the MD task, namely Misinformation Detection with Potential Commonsense Conflict.<n>We take inspiration from the prior studies indicating that fake articles are more likely to involve commonsense conflict.
• Score: 25.27903714824545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Misinformation Detection (MD), aiming to detect online misinformation automatically, emerges as a rapidly growing research topic in the community. In this paper, we propose a novel plug-and-play augmentation method for the MD task, namely Misinformation Detection with Potential Commonsense Conflict (MD-PCC). We take inspiration from the prior studies indicating that fake articles are more likely to involve commonsense conflict. Accordingly, we construct commonsense expressions for articles, serving to express potential commonsense conflicts inferred by the difference between extracted commonsense triplet and golden ones inferred by the well-established commonsense reasoning tool COMET. These expressions are then specified for each article as augmentation. Any specific MD methods can be then trained on those commonsense-augmented articles. Besides, we also collect a novel commonsense-oriented dataset named CoMis, whose all fake articles are caused by commonsense conflict. We integrate MD-PCC with various existing MD backbones and compare them across both 4 public benchmark datasets and CoMis. Empirical results demonstrate that MD-PCC can consistently outperform the existing MD baselines.

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