完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wu, Bo | en_US |
dc.contributor.author | Cheng, Wen-Huang | en_US |
dc.contributor.author | Zhang, Yongdong | en_US |
dc.contributor.author | Cao, Juan | en_US |
dc.contributor.author | Li, Jintao | en_US |
dc.contributor.author | Mei, Tao | en_US |
dc.date.accessioned | 2020-05-05T00:02:21Z | - |
dc.date.available | 2020-05-05T00:02:21Z | - |
dc.date.issued | 2020-03-01 | en_US |
dc.identifier.issn | 1041-4347 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1109/TKDE.2018.2889664 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/154160 | - |
dc.description.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. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Social network services | en_US |
dc.subject | Predictive models | en_US |
dc.subject | Prediction algorithms | en_US |
dc.subject | Computers | en_US |
dc.subject | Technological innovation | en_US |
dc.subject | Information processing | en_US |
dc.subject | Task analysis | en_US |
dc.subject | Microblogging | en_US |
dc.subject | key retweeter prediction | en_US |
dc.subject | information propagation | en_US |
dc.subject | user behavior | en_US |
dc.title | Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TKDE.2018.2889664 | en_US |
dc.identifier.journal | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | en_US |
dc.citation.volume | 32 | en_US |
dc.citation.issue | 3 | en_US |
dc.citation.spage | 547 | en_US |
dc.citation.epage | 559 | en_US |
dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
dc.identifier.wosnumber | WOS:000526526700010 | en_US |
dc.citation.woscount | 0 | en_US |
顯示於類別: | 期刊論文 |