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Unlocking Author Power: On the Exploitation of Auxiliary Author-Retweeter Relations for Predicting Key Retweeters.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Mar2020, Vol. 32 Issue 3, p547-559, 13p
- Publication Year :
- 2020
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
COGNITIVE radio
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 32
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 141599659
- Full Text :
- https://doi.org/10.1109/TKDE.2018.2889664