Evidential Independence Maximization on Twitter Network

Abstract : Detecting independent users in online social networks is an interesting research issue. In fact, independent users cannot generally be influenced, they are independent in their choices and decisions. Independent users may attract other users and make them adopt their point of view. A user is qualified as independent when his/her point of view does not depend on others ideas. Thus, the behavior of such a user is independent from other behaviors. Detecting independent users is interesting because a part of them can be influencers. Independent users that are not influencers can be directly targeted as they cannot be influenced. In this paper, we present an evidential independence maximization approach for Twitter users. The proposed approach is based on three metrics reflecting users behaviors. We propose an useful approach for detecting influencers. Indeed, we consider the independence as a characteristic of influencers even if not all independent users are influencers. The proposed approach is experimented on real data crawled from Twitter.
Type de document :
Communication dans un congrès
5th International Conference, Belief 2018, Sep 2018, Compiègne, France. Belief Functions: Theory and Applications
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01879620
Contributeur : Siwar Jendoubi <>
Soumis le : lundi 24 septembre 2018 - 10:37:30
Dernière modification le : mercredi 26 septembre 2018 - 11:21:20

Fichier

BELIEF-1.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01879620, version 1

Citation

Siwar Jendoubi, Mouna Chebbah, Arnaud Martin. Evidential Independence Maximization on Twitter Network. 5th International Conference, Belief 2018, Sep 2018, Compiègne, France. Belief Functions: Theory and Applications. 〈hal-01879620〉

Partager

Métriques

Consultations de la notice

54

Téléchargements de fichiers

11