Predicting answering behaviour in online question answering communities

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations Save citation

Open

Authors

Research units

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.

Documents

Details

Publication date24 Aug 2015
Publication titleHT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
Place of PublicationNew York, NY (US)
PublisherACM
Pages201-210
Number of pages10
ISBN (Print)978-1-4503-3395-5
Original languageEnglish
Event26th ACM Conference on Hypertext and Social Media - Guzelyurt, Cyprus

Conference

Conference26th ACM Conference on Hypertext and Social Media
Abbreviated titleHT 2015
CountryCyprus
CityGuzelyurt
Period1/09/154/09/15

Keywords

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

DOI

Download statistics

No data available

Employable Graduates; Exploitable Research

Copy the text from this field...