Detecting Influencial Users in Social Networks: Analysing Graph-Based and Linguistic Perspectives


Kévin Deturck, Namrata Patel, Pierre-Alain Avouac, Cédric Lopez, Damien Nouvel, Ioannis Partalas and Frédérique Segond (2019) Detecting influencial users in social networks: Analysing graph-based and linguistic perspectives, Artificial Intelligence for Knowledge Management, p. 113-131.

Résumé de l'article

The detection of influencers has met with increasing interest in the artificial intelligence community in recent years for its utility in singling out pertinent users within a large network of social media users. This could be useful, for example in commercial campaigns, to promote a product or a brand to a relevant target set of users. This task is performed either by analysing the graphical representation of user interactions in a social network or by measuring the impact of the linguistic content of user messages in online discussions. We independently explore both ways in the present paper with a hybridisation perspective. We extract structural information to highlight influence among interaction networks and identify linguistic traits of influential behaviours. We then compute a score of user influence using centrality measures with the structural information and a machine learning approach with the linguistic features.