Wu, BBWuWEN-HUANG CHENGZhang, YDYDZhangCao, JJCaoLi, JTJTLiMei, TTMei2023-02-212023-02-2120201041-4347https://scholars.lib.ntu.edu.tw/handle/123456789/628644Retweeting is a powerful driving force in information propagation on microblogging sites. However, identifying the most effective retweeters of a message (called the 'key retweeter prediction' problem) has become a significant research topic. Conventional approaches have addressed this topic from two main aspects: by analyzing either the personal attributes of microblogging users or the structures of user graph networks. However, according to sociological findings, author-retweeter dependencies also play a crucial role in influencing message propagation. In this paper, we propose a novel model to solve the key retweeter prediction problem by incorporating the auxiliary relations between a tweet author and potential retweeters. Without loss of generality, we formulate the relations from four relational factors: status relation, temporal relation, locational relation, and interactive relation. In addition, we propose a novel method, called 'Relation-based Learning to Rank (RL2R),' to determine the key retweeters for a given tweet by ranking the potential retweeters in terms of their spreadability. The experimental results show that our method outperforms the state-of-the-art algorithms at top-k retweeter prediction, achieving a significant relative average improvement of 19.7-29.4 percent. These findings provide new insights for understanding user behaviors on social media for key retweeter prediction purposes.Social network services; Predictive models; Prediction algorithms; Computers; Technological innovation; Information processing; Task analysis; Microblogging; key retweeter prediction; information propagation; user behavior; INFLUENTIAL SPREADERS; NETWORKSjournal article10.1109/TKDE.2018.28896642-s2.0-85059285182WOS:000526526700010https://api.elsevier.com/content/abstract/scopus_id/85059285182