Open Access
September 2020 PTEM: A popularity-based topical expertise model for community question answering
Hohyun Jung, Jae-Gil Lee, Namgil Lee, Sung-Ho Kim
Ann. Appl. Stat. 14(3): 1304-1325 (September 2020). DOI: 10.1214/20-AOAS1346


Community Question Answering (CQA) websites are widely used in sharing knowledge, where users can ask questions, reply answers and evaluate answers. So far, the evaluation of answers has been explained by the contents of answers through the investigation of users’ topics of interest and expertise levels. In this paper we focus on modeling the user’s evaluation behavior, in that users can see the answerer’s profile as well as the answer content before evaluating the quality of the answer. We propose a model called Popularity-based Topical Expertise Model (PTEM), a generative model to analyze the rich-get-richer phenomenon that popular user’s answers are more recommended. We can simultaneously estimate the topical expertise of each user and the strength of the rich-get-richer effect through the EM algorithm combined with collapsed Gibbs sampling. Experiments are performed on the StackExchange data, and the results demonstrate a rich-get-richer phenomenon in the community. We further discuss the superiority and usefulness of the proposed model through analysis in the discipline of philosophy.


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Hohyun Jung. Jae-Gil Lee. Namgil Lee. Sung-Ho Kim. "PTEM: A popularity-based topical expertise model for community question answering." Ann. Appl. Stat. 14 (3) 1304 - 1325, September 2020.


Received: 1 August 2019; Revised: 1 February 2020; Published: September 2020
First available in Project Euclid: 18 September 2020

MathSciNet: MR4152134
Digital Object Identifier: 10.1214/20-AOAS1346

Keywords: community question answering , EM algorithm , rich get richer , Topic analysis , user behavior

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.14 • No. 3 • September 2020
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