{"type":"ScholarlyArticle","title":"Unified Evidence Enhancement Inference Framework for Fake News Detection","authors":[{"name":"Lianwei Wu","slug":"lianwei-wu","given":"Lianwei","family":"Wu","affiliation":"ASGO , School of Computer Science , Northwestern Polytechnical University , Xi'an , China; Research & Development Institute of Northwestern Polytechnical University in Shenzhen"},{"name":"Linyong Wang","slug":"linyong-wang","given":"Linyong","family":"Wang","affiliation":"ASGO , School of Computer Science , Northwestern Polytechnical University , Xi'an , China; Research & Development Institute of Northwestern Polytechnical University in Shenzhen"},{"name":"Yongqiang Zhao","slug":"yongqiang-zhao","given":"Yongqiang","family":"Zhao","affiliation":"School of Computer Science , Peking University , Beijing , China"}],"abstract":"The current approaches for fake news detection are mainly devoted to extracting candidate evidence from comments (or external articles) and establishing interactive reasoning with the news itself to verify the falsehood of the news. However, they still have several drawbacks: 1) The interaction object is coarse-grained, which mainly drives the entire news to participate in interaction, but ignores the learning of potential suspicious segments in news; 2) The reasoning ways are relatively single, making it difficult to explore the various possible correlations between news and candidate evidence. To this end, we propose Unified Evidence Enhancement Inference framework (UEEI) to discover and infer high-quality evidence to reveal the false parts of news for detection. Specifically, UEEI first promotes the interaction fusion between comments and news from the perspectives of semantic and emotion, thereby learning potential suspicious fragments in news. Then, the model constructs entity-level and relationship-level retrievals to screen sufficient candidate evidence from external sources. Finally, we measure coherence between suspicious fragments and candidate evidence by multi-view reasoning, and further infer explainable evidence that discovers the false parts of news. Experiments on three public datasets confirm the effectiveness and interpretability of our UEEI.","doi":"10.24963/ijcai.2024/723","url":"https://ijcai.org/proceedings/2024/723","pdf":"https://ijcai.org/proceedings/2024/0723.pdf","markdown":"https://ijcai.org/proceedings/2024/723.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/723","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":723,"pages":{"first":6541,"last":6549},"keywords":["Game Theory and Economic Paradigms: GTEP: Computational social choice","Natural Language Processing: NLP: Applications","Multidisciplinary Topics and Applications: MTA: Social sciences"],"videos":[],"references":[{"position":1,"raw":"[Cambria et al., 2020] Erik Cambria, Yang Li, Frank Z Xing, Soujanya Poria, and Kenneth Kwok. 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