Predicting answering behaviour in online question answering communities

Grégoire Burel, Paul Mulholland, Yulan He, Harith Alani

Research output: Chapter in Book/Published conference outputConference publication


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.

Original languageEnglish
Title of host publicationHT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
Place of PublicationNew York, NY (US)
Number of pages10
ISBN (Print)978-1-4503-3395-5
Publication statusPublished - 24 Aug 2015
Event26th ACM Conference on Hypertext and Social Media - Guzelyurt, Cyprus
Duration: 1 Sept 20154 Sept 2015


Conference26th ACM Conference on Hypertext and Social Media
Abbreviated titleHT 2015


  • online communities
  • social media
  • social Q&A platforms
  • user behaviour


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