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Predicting Answering Behaviour in Online Question Answering Communities

Burel, Gregoire; Mulholland, Paul; He, Yulan and Alani, Harith (2015). Predicting Answering Behaviour in Online Question Answering Communities. In: HT '15 Proceedings of the 26th ACM Conference on Hypertext & Social Media, ACM, pp. 201–210.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1145/2700171.2791041
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Abstract

The value of Question Answering (Q&A) communities is de- pendent on members of the community finding the questions they are most willing and able to answer. This can be diffi- cult 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 com- munity. 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 influence users’ question selections.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 ACM
ISBN: 1-4503-3395-8, 978-1-4503-3395-5
Keywords: social Q&A platforms; online communities; user behaviour; social media
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Centre for Policing Research and Learning (CPRL)
Related URLs:
Item ID: 44262
Depositing User: Kay Dave
Date Deposited: 09 Sep 2015 08:08
Last Modified: 01 Nov 2017 09:39
URI: http://oro.open.ac.uk/id/eprint/44262
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