{"type":"ScholarlyArticle","title":"Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers","authors":[{"name":"Buyun He","slug":"buyun-he","given":"Buyun","family":"He","affiliation":"University of Science and Technology of China"},{"name":"Yingguang Yang","slug":"yingguang-yang","given":"Yingguang","family":"Yang","affiliation":"University of Science and Technology of China"},{"name":"Qi Wu","slug":"qi-wu","given":"Qi","family":"Wu","affiliation":"University of Science and Technology of China"},{"name":"Hao Liu","slug":"hao-liu","given":"Hao","family":"Liu","affiliation":"University of Science and Technology of China"},{"name":"Renyu Yang","slug":"renyu-yang","given":"Renyu","family":"Yang","affiliation":"Beihang University"},{"name":"Hao Peng","slug":"hao-peng","given":"Hao","family":"Peng","affiliation":"Beihang University; Harbin Engineering University"},{"name":"Xiang Wang","slug":"xiang-wang","given":"Xiang","family":"Wang","affiliation":"University of Science and Technology of China"},{"name":"Yong Liao","slug":"yong-liao","given":"Yong","family":"Liao","affiliation":"University of Science and Technology of China"},{"name":"Pengyuan Zhou","slug":"pengyuan-zhou","given":"Pengyuan","family":"Zhou","affiliation":"Aarhus university"}],"abstract":"Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions. While earlier graph-based approaches, which leverage topological structure of social networks, yielded notable outcomes, they overlooked the inherent dynamicity of social networks -- In reality, they largely depicted the social network as a static graph and solely relied on its most recent state. Due to the absence of dynamicity modeling, such approaches are vulnerable to evasion, particularly when advanced social bots interact with other users to camouflage identities and escape detection. To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network. Specifically, we characterize a social network as a dynamic graph. A structural module is employed to acquire topological information from each historical snapshot. Additionally, a temporal module is proposed to integrate historical context and model the evolving behavior patterns exhibited by social bots and legitimate users. Experimental results demonstrate the superiority of BotDGT against the leading methods that neglected the dynamic nature of social networks in terms of accuracy, recall, and F1-score.","doi":"10.24963/ijcai.2024/646","url":"https://ijcai.org/proceedings/2024/646","pdf":"https://ijcai.org/proceedings/2024/0646.pdf","markdown":"https://ijcai.org/proceedings/2024/646.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/646","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":"Multidisciplinary Topics and Applications","track":"Main Track","number":646,"pages":{"first":5844,"last":5852},"keywords":["Multidisciplinary Topics and Applications: MTA: Web and social networks","Data Mining: DM: Mining text, web, social media","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Ali Alhosseini et al., 2019] Seyed Ali Alhosseini, Raad Bin Tareaf, Pejman Najafi, and Christoph Meinel. 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