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HAR-search: A Method to Discover Hidden Affinity Relationships in Online Communities [r-libre/1721]

Tshimula, Jean-Marie; Chikhaoui, Belkacem, & Wang, Shengrui (In Press). HAR-search: A Method to Discover Hidden Affinity Relationships in Online Communities. In The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019). Vancouver, Canada : IEEE/ACM.

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  PDF - HARsearchASONAM2019-up.pdf
Content : Accepted Version
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Item Type: Papers in Conference Proceedings
Refereed: Yes
Status: In Press
Abstract: This paper addresses the problem of discovering hidden affinity relationships in online communities. Online discussions assemble people to talk about various types of topics and to share information. People progressively develop the affinity, and they get closer as frequently as they mention themselves in messages and they send positive messages to one another. We propose an algorithm, named HAR-search, for discovering hidden affinity relationships between individuals. Based on Markov Chain Models, we derive the affinity scores amongst individuals in an online community. We show that our method allows to track the evolution of the affinity over time and to predict affinity relationships arisen from the influence of certain community members. The comparison with the state-of-the-art method shows that our method results in robust discovery and considers minute details.
Depositor: Chikhaoui, Belkacem
Owner / Manager: Belkacem Chikhaoui
Deposited: 28 Jun 2019 15:24
Last Modified: 28 Jun 2019 15:24

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