Joint Model of Topics, Expertises, Activities and Trends for Question Answering Web Applications

Abstract

Users in question-answer sites generate huge amounts of high quality and highly reusable information. This information can be categorized by topics but since users' interests change with time, uncovering the temporal patterns and trends in their activity is of prime interest to detect their current expertize. These temporal variations have long remained unexplored in question-answer sites while detecting them enables us to improve tasks such as: question routing, expert recommending and community life-cycle management. In this paper, we propose a generative model of such a community and its dynamics, and we perform experiments with real-world data extracted from the StackOverflow website to confirm the effectiveness of our model to study the users' behaviors and topics dynamics.