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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Communication dans un congrès
21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Sep 2017, Marseille, France. Published by Elsevier B.V
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https://hal.archives-ouvertes.fr/hal-01568061
Contributeur : Dorra Attiaoui <>
Soumis le : jeudi 24 août 2017 - 11:17:29
Dernière modification le : samedi 26 août 2017 - 01:08:26

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k17gen-127.pdf
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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. Published by Elsevier B.V. <hal-01568061>

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