Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters

doi:10.1109/TKDE.2018.2889664

	Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters
	Wu, Bo 1,2; Cheng, Wen-Huang 3,4; Zhang, Yongdong 5; Cao, Juan 1; Li, Jintao 1; Mei, Tao 6
	2020-03-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	32 期号:3 页码:547-559
摘要	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.
关键词	Social network services Predictive models Prediction algorithms Computers Technological innovation Information processing Task analysis Microblogging key retweeter prediction information propagation user behavior
DOI	10.1109/TKDE.2018.2889664
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB0800403] ; National Nature Science Foundation of China[61525206] ; National Defense Science and Technology Fund for Distinguished Young Scholars[2017JCJQ-ZQ-022] ; Ministry of Science and Technology of Taiwan[MOST-105-2628-E-009-008-MY3] ; Ministry of Science and Technology of Taiwan[MOST-107-2218-E-009-062] ; Ministry of Science and Technology of Taiwan[MOST-107-2218-E-002-054] ; Ministry of Science and Technology of Taiwan[MOST-107-2221-E-182-025MY2]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000526526700010
出版者	IEEE COMPUTER SOC
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14233
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yongdong
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Natl Chiao Tung Univ, Dept Elect Engn, Hsinchu 30010, Taiwan 4.Natl Chiao Tung Univ, Inst Elect, Hsinchu 30010, Taiwan 5.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China 6.JD AI Res, Beijing 100105, Peoples R China
推荐引用方式 GB/T 7714	Wu, Bo,Cheng, Wen-Huang,Zhang, Yongdong,et al. Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2020,32(3):547-559.
APA	Wu, Bo,Cheng, Wen-Huang,Zhang, Yongdong,Cao, Juan,Li, Jintao,&Mei, Tao.(2020).Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,32(3),547-559.
MLA	Wu, Bo,et al."Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 32.3(2020):547-559.