research-article

Incorporating Social Role Theory into Topic Models for Social Media Content Analysis

Authors:

Wayne Xin Zhao,

Xiaoming LiAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 27, Issue 4

Pages 1032 - 1044

https://doi.org/10.1109/TKDE.2014.2359672

Published: 01 April 2015 Publication History

Abstract

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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Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 27, Issue 4

April 2015

274 pages

ISSN:1041-4347

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Copyright © 2014.

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IEEE Educational Activities Department

United States

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Published: 01 April 2015

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Zhang AChen YSheng LWang XChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On Generative Agents in RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657844(1807-1817)Online publication date: 10-Jul-2024
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Du YWang YHu JLi XChen X(2022)An emotion role mining approach based on multiview ensemble learning in social networksInformation Fusion10.1016/j.inffus.2022.07.01088:C(100-114)Online publication date: 1-Dec-2022
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