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Conference papers
Detecting Influencial Users in Social Networks: Analysing Graph-Based and Linguistic Perspectives
Abstract : There has been increasing interest in the artificial intelligence community for influencer detection 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 graph-based representation of user interactions in a social network or by measuring the impact of the linguistic content of user messages in online discussions. We performed independent studies for each of these methods in the present paper with a hybridisation perspective. In the first study, we extract structural information to highlight influence among interaction networks. In the second, we identify linguistic features of influential behaviours. We then compute a score of user influence using centrality measures with the structural information for the former and a machine learning approach based on the relevant linguistic features for the latter.
Cited literature [24 references]
https://hal.inria.fr/hal-02517698
Contributor : Hal Ifip <>
Submitted on : Tuesday, March 24, 2020 - 5:05:22 PM
Last modification on : Monday, December 7, 2020 - 7:40:01 PM
Long-term archiving on: : Thursday, June 25, 2020 - 2:42:24 PM
Contributor : Hal Ifip <>
Submitted on : Tuesday, March 24, 2020 - 5:05:22 PM
Last modification on : Monday, December 7, 2020 - 7:40:01 PM
Long-term archiving on: : Thursday, June 25, 2020 - 2:42:24 PM
Licence
Distributed under a Creative Commons Attribution 4.0 International License