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Ranking Online Social Users by their Influence

Abstract : We introduce an original mathematical model to analyse the diffusion of posts within a generic online social platform. The main novelty is that each user is not simply considered as a node on the social graph, but is further equipped with his own Wall and Newsfeed, and has his own self-posting and re-posting activity. As a main result using our developed model, we derive in closed form the probabilities that posts originating from a given user are found on the Wall and Newsfeed of any other. These are the solution of a linear system of equations. Comparisons with simulations show the accuracy of our model and its robustness with respect to the modelling assumptions. Using the probabilities from the solution we define a new measure of per-user influence over the entire network, the Ψ-score, which combines the user position on the graph with the user (re-)posting activity. Furthermore, we compare the new model and its Ψ-score against the empirical influence measured from very large data traces (Twitter, Weibo). The results illustrate that these new tools can accurately rank influencers for such real world applications.
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Contributor : Anastasios Giovanidis <>
Submitted on : Saturday, October 17, 2020 - 4:39:54 PM
Last modification on : Wednesday, October 21, 2020 - 3:17:57 AM


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  • HAL Id : hal-02970215, version 1


Anastasios Giovanidis, Bruno Baynat, Clémence Magnien, Antoine Vendeville. Ranking Online Social Users by their Influence. 2020. ⟨hal-02970215⟩



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