Influence Assessment in Twitter Multi-Relational Network

Lobna Azaza (1) , Sergey Kirkizov, (1) , Marinette Savonnet (1) , Eric Leclercq (1) , Rim Faiz (2)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus

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.

Mots clés: Twitter network; Influence; Information fusion; Belief theory

Domaines: Informatique [cs]; Sciences de l'ingénieur [physics]; Electronique

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⟩