Predicting answering behaviour in online question answering communities

Abstract

The value of Question Answering (Q&A) communities is dependent on members of the community finding the questions they are most willing and able to answer. This can be difficult in communities with a high volume of questions. Much previous has work attempted to address this problem by recommending questions similar to those already answered. However, this approach disregards the question selection behaviour of the answers and how it is affected by factors such as question recency and reputation. In this paper, we identify the parameters that correlate with such a behaviour by analysing the users' answering patterns in a Q&A community. We then generate a model to predict which question a user is most likely to answer next. We train Learning to Rank (LTR) models to predict question selections using various user, question and thread feature sets. We show that answering behaviour can be predicted with a high level of success, and highlight the particular features that inuence users' question selections.

Publication DOI: https://doi.org/10.1145/2700171.2791041
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Event Title: 26th ACM Conference on Hypertext and Social Media
Event Type: Other
Event Dates: 2015-09-01 - 2015-09-04
Uncontrolled Keywords: online communities,social media,social Q&A platforms,user behaviour,Artificial Intelligence,Software,Computer Graphics and Computer-Aided Design,Human-Computer Interaction
ISBN: 978-1-4503-3395-5
Last Modified: 05 Jan 2024 18:08
Date Deposited: 23 Feb 2016 13:00
Full Text Link: http://dl.acm.o ... 2700171.2791041
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2015-08-24
Authors: Burel, Grégoire
Mulholland, Paul
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Alani, Harith

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Version: Accepted Version


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