Microblogging Content Propagation Modeling Using Topic-Specific Behavioral Factors

• Published in: IEEE transactions on knowledge and data engineering Vol. 28; no. 9; pp. 2407 - 2422
• Main Authors: Hoang, Tuan-Anh, Lim, Ee-Peng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Content propagation; Data models; Factorization; Learning; Mathematical analysis; Mathematical models; microblogging; Numerical models; Predictive models; Receivers; Social networks; State of the art; susceptibility; Tensile stress; Tensors; Twitter; user behavior; virality
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2016.2562628

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.