When a microblogging user adopts some content propagated to her, we can attribute that to three behavioral factors, namely, topic virality, user virality, and user susceptibility. Topic virality measures the degree to which a topic attracts propagations by users. User virality and susceptibility refer to the ability of a user to propagate content to other users, and the propensity of a user adopting content propagated to her, respectively. In this paper, we study the problem of mining these behavioral factors specific to topics from microblogging content propagation data. We first construct a three dimensional tensor for representing the propagation instances. We then propose a tensor factorization framework to simultaneously derive the three sets of behavioral factors. Based on this framework, we develop a numerical factorization model and another probabilistic factorization variant. We also develop an efficient algorithm for the models' parameters learning. Our experiments on a large Twitter dataset and synthetic datasets show that the proposed models can effectively mine the topic-specific behavioral factors of users and tweet topics. We further demonstrate that the proposed models consistently outperforms the other state-of-the-art content based models in retweet prediction over time.
Content propagation, virality, susceptibility, user behavior, microblogging
Computer Sciences | Databases and Information Systems | Social Media
Data Management and Analytics
IEEE Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers (IEEE)
HOANG TUAN ANH and Ee-peng LIM.
Microblogging content propagation modeling using topic-specific behavioral factors. (2016). IEEE Transactions on Knowledge and Data Engineering. 28, (9), 2407-2422. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3573
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