{"type":"ScholarlyArticle","title":"Rumor Detection on Social Media with Graph Structured Adversarial Learning","authors":[{"name":"Xiaoyu Yang","slug":"xiaoyu-yang","given":"Xiaoyu","family":"Yang","affiliation":"{littlehaes , lvyuefei , tiantian 96727"},{"name":"Yuefei Lyu","slug":"yuefei-lyu","given":"Yuefei","family":"Lyu","affiliation":"{littlehaes , lvyuefei , tiantian 96727"},{"name":"Tian Tian","slug":"tian-tian","given":"Tian","family":"Tian","affiliation":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) , Ministry of Education , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Yifei Liu","slug":"yifei-liu","given":"Yifei","family":"Liu","affiliation":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) , Ministry of Education , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Yudong Liu","slug":"yudong-liu","given":"Yudong","family":"Liu","affiliation":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) , Ministry of Education , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Xi Zhang","slug":"xi-zhang","given":"Xi","family":"Zhang","affiliation":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) , Ministry of Education , Beijing University of Posts and Telecommunications , Beijing , China"}],"abstract":"The wide spread of rumors on social media has caused tremendous effects in both the online and offline world. In addition to text information, recent detection methods began to exploit the graph structure in the propagation network. However, without a rigorous design, rumors may evade such graph models using various camouflage strategies by perturbing the structured data. Our focus in this work is to develop a robust graph-based detector to identify rumors on social media from an adversarial perspective. We first build a heterogeneous information network to model the rich information among users, posts, and user comments for detection. We then propose a graph adversarial learning framework, where the attacker tries to dynamically add intentional perturbations on the graph structure to fool the detector, while the detector would learn more distinctive structure features to resist such perturbations. In this way, our model would be enhanced in both robustness and generalization. Experiments on real-world datasets demonstrate that our model achieves better results than the state-of-the-art methods.","doi":"10.24963/ijcai.2020/197","url":"https://ijcai.org/proceedings/2020/197","pdf":"https://ijcai.org/proceedings/2020/0197.pdf","markdown":"https://ijcai.org/proceedings/2020/197.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/197","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":197,"pages":{"first":1417,"last":1423},"keywords":["Data Mining: Classification, Semi-Supervised Learning","Data Mining: Applications","Data Mining: Mining Graphs, Semi Structured Data, Complex Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24151/file_24151.m3u8","length_sec":290,"provider":"hls"}],"references":[{"position":1,"raw":"[Allcott and Gentzkow, 2017] Hunt Allcott and Matthew Gentzkow. 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