{"type":"ScholarlyArticle","title":"Modeling Personalized Retweeting Behaviors for Multi-Stage Cascade Popularity Prediction","authors":[{"name":"Mingyang Zhou","slug":"mingyang-zhou","given":"Mingyang","family":"Zhou","affiliation":"Guangdong Province Key Laboratory of Popular High Performance Computers , College of Computer Science and Software Engineering , Shenzhen University , China"},{"name":"Yanjie Lin","slug":"yanjie-lin","given":"Yanjie","family":"Lin","affiliation":"Guangdong Province Key Laboratory of Popular High Performance Computers , College of Computer Science and Software Engineering , Shenzhen University , China"},{"name":"Gang Liu","slug":"gang-liu","given":"Gang","family":"Liu","affiliation":"Guangdong Province Key Laboratory of Popular High Performance Computers , College of Computer Science and Software Engineering , Shenzhen University , China"},{"name":"Zuwen Li","slug":"zuwen-li","given":"Zuwen","family":"Li","affiliation":"Guangdong Province Key Laboratory of Popular High Performance Computers , College of Computer Science and Software Engineering , Shenzhen University , China"},{"name":"Hao Liao","slug":"hao-liao","given":"Hao","family":"Liao","affiliation":"Guangdong Province Key Laboratory of Popular High Performance Computers , College of Computer Science and Software Engineering , Shenzhen University , China"},{"name":"Rui Mao","slug":"rui-mao","given":"Rui","family":"Mao","affiliation":"Guangdong Province Key Laboratory of Popular High Performance Computers , College of Computer Science and Software Engineering , Shenzhen University , China"}],"abstract":"Predicting the size of message cascades is critical in various applications, such as online advertising and early detection of rumors. However, most existing deep learning approaches rely on cascade observation, which hinders accurate cascade prediction before message posting. Besides, these approaches overlook personalized retweeting behaviors that reflect users' inclination to retweeting specific types of information. In this study, we propose a universal cascade prediction framework, namely Cascade prediction regarding Multiple Stage (CasMS), that effectively predicts cascade popularity across message generation stage as well as short-term and long-term stages. Unlike previous methods, our approach not only captures users' personalized retweeting behaviors but also incorporates temporal cascade features. We perform the experiments in datasets collected ourselves as well as public datasets. The results show that our method significantly surpasses existing approaches in predicting the cascade during the message generation stage and different time periods in the cascade dynamics.","doi":"10.24963/ijcai.2024/287","url":"https://ijcai.org/proceedings/2024/287","pdf":"https://ijcai.org/proceedings/2024/0287.pdf","markdown":"https://ijcai.org/proceedings/2024/287.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/287","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":"Data Mining","track":"Main Track","number":287,"pages":{"first":2598,"last":2606},"keywords":["Data Mining: DM: Recommender systems","Machine Learning: ML: Applications","Data Mining: DM: Mining text, web, social media"],"videos":[],"references":[{"position":1,"raw":"[Bian et al., 2020] Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, and Junzhou Huang. 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