Influence Assessment in Twitter Multi-Relational Network

Lobna Azaza 1, * Sergey Kirkizov, 1, * Marinette Savonnet 1, * Eric Leclercq 1, * Rim Faiz 2, *
* Auteur correspondant
1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
2 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
Abstract : Influence in Twitter has become recently a hot research topic since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this paper, we propose a new approach for influence assessment on Twitter network, it is based on a modified version of the conjunctive combination rule in belief functions theory in order to combine different influence markers such as retweets, mentions and replies. We experiment the proposed method on a large amount of data gathered from Twitter in the context of the European Elections 2014 and deduce top influential candidates.
Keywords : Belief theory Influence Information fusion Twitter network
Type de document :
Communication dans un congrès
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01436493
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : lundi 16 janvier 2017 - 14:37:43
Dernière modification le : mardi 5 novembre 2019 - 11:10:10

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Lobna Azaza, Sergey Kirkizov,, Marinette Savonnet, Eric Leclercq, Rim Faiz. Influence Assessment in Twitter Multi-Relational Network. 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), University of Bourgogne; University of Milan, Nov 2015, Bangkok, Thailand. pp. 436-443, ⟨10.1109/SITIS.2015.82⟩. ⟨hal-01436493⟩

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