Automatic role detection in online forums

Abstract : This thesis addresses the problem of detecting user roles in online discussion forums. A role may be defined as the set of behaviors characteristic of a person or a position. In discussion forums, behaviors are primarily observed through conversations. Hence, we focus our attention on how users discuss. We propose three methods to detect groups of users with similar conversational behaviors.Our first method for the detection of roles is based on conversational structures. Weapply different notions of neighborhood for posts in tree graphs (radius-based, order-based, and time-based) and compare the conversational patterns that they detect as well as the clusters of users with similar conversational patterns.Our second method is based on stochastic models of growth for conversation threads.Building upon these models we propose a method to find groups of users that tend to reply to the same type of posts. We show that, while there are clusters of users with similar replying patterns, there is no strong evidence that these behaviors are predictive of future behaviors |except for some groups of users with extreme behaviors.In out last method, we integrate the type of data used in the two previous methods(feature-based and behavioral or functional-based) and show that we can find clusters using fewer examples. The model exploits the idea that users with similar features have similar behaviors.
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Submitted on : Wednesday, January 18, 2017 - 3:25:07 PM
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  • HAL Id : tel-01439342, version 1


Alberto Lumbreras. Automatic role detection in online forums. Social and Information Networks [cs.SI]. Université de Lyon, 2016. English. ⟨NNT : 2016LYSE2111⟩. ⟨tel-01439342⟩



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