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Theses

Belief Detection and Temporal Analysis of Experts in Question Answering Communities: case study Stack Overflow

Abstract : During the last decade, people have changed the way they seek information online. Between question answering communities, specialized websites, social networks, the Web has become one of the most widespread platforms for information exchange and retrieval. Question answering communities provide an easy and quick way to search for information needed in any topic. The user has to only ask a question and wait for the other members of the community to respond. Any person posting a question intends to have accurate and helpful answers. Within these platforms, we want to find experts. They are key users that share their knowledge with the other members of the community. Expert detection in question answering communities has become important for several reasons such as providing high quality content, getting valuable answers, etc. In this thesis, we are interested in proposing a general measure of expertise based on the theory of belief functions. Also called the mathematical theory of evidence, it is one of the most well known approaches for reasoning under uncertainty. In order to identify experts among other users in the community, we have focused on finding the most important features that describe every individual. Next, we have developed a model founded on the theory of belief functions to estimate the general expertise of the contributors. This measure will allow us to classify users and detect the most knowledgeable persons. Therefore, once this metric defined, we look at the temporal evolution of users' behavior over time. We propose an analysis of users activity for several months in community. For this temporal investigation, we will describe how do users evolve during their time spent within the platform. Besides, we are also interested on detecting potential experts during the beginning of their activity. The effectiveness of these approaches is evaluated on real data provided from Stack Overflow.
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https://hal.archives-ouvertes.fr/tel-01743002
Contributor : Dorra Attiaoui <>
Submitted on : Monday, April 2, 2018 - 12:50:30 PM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM

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  • HAL Id : tel-01743002, version 1

Citation

Dorra Attiaoui. Belief Detection and Temporal Analysis of Experts in Question Answering Communities: case study Stack Overflow. Computer Science [cs]. Université de Rennes 1, 2017. English. ⟨tel-01743002⟩

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