Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence; and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively.
A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Hong Kong Baptist University（香港浸会大学）；Guangdong Institute of Smart Education, Jinan University（暨南大学广东智慧教育研究院）；University of Science and Technology Beijing（北京科技大学）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence（知识驱动人机智能教育部工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际合作中心）
MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval
University of Sheffield（谢菲尔德大学）；University of Science and Technology Beijing（北京科技大学）；University of California San Diego（加州大学圣地亚哥分校）；The Pennsylvania State University（宾夕法尼亚州立大学）
CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（上海交通大学计算机科学学院）；Shanghai KEY Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China（上海信息安全集成管理技术重点实验室）；School of Politics, National Defense University, Shanghai, China（国防大学政治学院）
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
University of South Carolina（南卡罗来纳大学）；IUT（伊斯兰科技大学）；IIT Gandhinagar（甘地纳格尔印度理工学院）；BIT Mesra（贝拉理工学院梅斯拉校区）；Stanford University（斯坦福大学）；Amazon AI（亚马逊人工智能研究院）
