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Belief Measure of Expertise for Experts Detection in Question Answering Communities: case study Stack Overflow

Abstract : Online Question Answering Communities (Q& A C) provide a valuable amount of information in several topics. The major challenge with Q& A C is the detection of the authoritative users. When manipulating real world data, we have to deal with imperfections and uncertainty that can occur. In this paper, we propose a belief measure of expertise allowing us to detect users with the highest degree of expertise based on their attributes. Experiments on a dataset from a large online Q&A Community prove that the proposed model can be used to improve the identification of most expert users.
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https://hal.archives-ouvertes.fr/hal-01568061
Contributor : Dorra Attiaoui <>
Submitted on : Thursday, August 24, 2017 - 11:17:29 AM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM

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  • HAL Id : hal-01568061, version 1

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Dorra Attiaoui, Arnaud Martin, Boutheina Ben Yaghlane. Belief Measure of Expertise for Experts Detection in Question Answering Communities: case study Stack Overflow. 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Sep 2017, Marseille, France. ⟨hal-01568061⟩

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