User Behavior Mining in Microblogging

• Main Author: HOANG, Tuan Anh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: microblogging; user behavior; information propagation; virality; topic modelling; topical community; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/etd_coll/130; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1129&context=etd_coll
• Institution: Singapore Management University

This dissertation addresses the modeling of factors concerning microblogging users' content and behavior. We focus on two sets of factors. The first set includes behavioral factors of users and content items driving content propagation in microblogging. The second set consists of latent topics and communities of users as the users are engaged in content generation and behavior adoptions. These two sets of factors are extremely important in many applications, e.g., network monitoring and recommender systems. In the first part of this dissertation, we identify user virality, user susceptibility, and content virality as three behavioral factors that affect users' behaviors in content propagation. User virality refers to the ability of a user in getting her content propagated by many other users, while user susceptibility refers to the tendency of a user to propagate other users' content. Content virality refers to the tendency of a content item to attract propagation by users. Instead of modeling these factors independently as done in previous research, we propose to jointly model all these factors considering their inter-relationships. We develop static, temporal, and incremental models for measuring the factors based on propagation data. We also develop a static model for modeling the factors specific to topics. In the second part of this dissertation, we develop topic models for learning users' topical interest and communities from both their content and behavior. We first propose a model to derive community affiliations of users using topics and sentiments expressed in their content as well as their behavior. We then extend the model to learn both users' personal interest and that of their communities, distinguishing the two types of interests. Our model also learns the bias of users toward their communities when generating content and adopting behavior.\302\240