Incorporating social role theory into topic models for social media content analysis


In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

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Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: National Key Basic Research Program (973 Program) of China under Grant No.2014CB340403; the National Natural Science Foundation of China under Grant No. M13210007; 973 Program with Grant No.2014CB340405 and NSFC Grant 61272340; EPSRC Grant EP/L010690/1.
Uncontrolled Keywords: Computational Theory and Mathematics,Information Systems,Computer Science Applications
Publication ISSN: 1558-2191
Last Modified: 01 Mar 2024 08:11
Date Deposited: 22 Apr 2015 15:15
Full Text Link: http://ieeexplo ... rnumber=6906267
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2015-04-01
Authors: Zhao, Wayne Xin
Wang, Jinpeng
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Nie, Jian-Yun
Wen, Ji-Rong
Li, Xiaoming



Version: Accepted Version

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