https://scholars.lib.ntu.edu.tw/handle/123456789/628644
Title: | Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters | Authors: | Wu, B WEN-HUANG CHENG Zhang, YD Cao, J Li, JT Mei, T |
Keywords: | Social network services; Predictive models; Prediction algorithms; Computers; Technological innovation; Information processing; Task analysis; Microblogging; key retweeter prediction; information propagation; user behavior; INFLUENTIAL SPREADERS; NETWORKS | Issue Date: | 2020 | Publisher: | IEEE COMPUTER SOC | Journal Volume: | 32 | Journal Issue: | 3 | Start page/Pages: | 547 | Source: | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | Abstract: | Retweeting 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/628644 | ISSN: | 1041-4347 | DOI: | 10.1109/TKDE.2018.2889664 |
Appears in Collections: | 資訊工程學系 |
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