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Information fusion-based approach for studying influence on Twitter using belief theory

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 study, we propose a new approach for multi-level influence assessment on multi-relational networks, such as Twitter. We define a social graph to model the relationships between users as a multiplex graph where users are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies). We explore how relations between nodes in this graph could reveal about the influence degree and propose a generic computational model to assess influence degree of a certain node. This is based on the conjunctive combination rule from the belief functions theory to combine different types of relations. We experiment the proposed method on a large amount of data gathered from Twitter during the European Elections 2014 and deduce top influential candidates. The results show that our model is flexible enough to to consider multiple interactions combination according to social scientists needs or requirements and that the numerical results of the belief theory are accurate. We also evaluate the approach over the CLEF RepLab 2014 data set and show that our approach leads to quite interesting results.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01857513
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : jeudi 16 août 2018 - 15:00:00
Dernière modification le : vendredi 17 juillet 2020 - 14:59:07

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Lobna Azaza, Sergey Kirgizov, Marinette Savonnet, Eric Leclercq, Nicolas Gastineau, et al.. Information fusion-based approach for studying influence on Twitter using belief theory. Computational Social Networks, Springer, 2016, 3 (1), pp.5. ⟨10.1186/s40649-016-0030-2⟩. ⟨hal-01857513⟩

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