Opinion Manipulation in Social Networks

Silva, Alonso

doi:10.1007/978-3-319-51034-7_18

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Theorem 4. For the two cases of Lemma 2 and any initialization vector p i (0), the discrete-time linear system (11) converges towards the fixed-point solution (9) when t → ∞.…”

Section: Fixed-point Algorithmmentioning

confidence: 96%

Performance Analysis of Online Social Platforms

Giovanidis

Baynat

Vendeville

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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We introduce an original mathematical model to analyze the diffusion of posts within a generic online social platform. Each user of such a platform has his own Wall and Newsfeed, as well as 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 probabilities are the solution of a linear system of equations. Conditions of existence of the solution are provided, and two ways of solving the system are proposed, one using matrix inversion and another using fixed-point iteration. Comparisons with simulations show the accuracy of our model and its robustness with respect to the modeling assumptions. Hence, this article introduces a novel measure which allows to rank users by their influence on the social platform, by taking into account not only the social graph structure, but also the platform design, user activity (self-and re-posting), as well as competition among posts.

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“…Theorem 4. For the two cases of Lemma 2 and any initialization vector p i (0), the discrete-time linear system (11) converges towards the fixed-point solution (9) when t → ∞.…”

Section: Fixed-point Algorithmmentioning

confidence: 96%

Performance Analysis of Online Social Platforms

Giovanidis

Baynat

Vendeville

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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“…Another paper that is very closely related to our work is that of Silva [49], where the author studies a similar bribery model, but for cardinal preferences expressed as numbers between 0 and 1 (e.g., corresponding to the level of support for the current government). In his model, these values can be modified through bribery actions, after which a certain dynamic process (i.e., a form of diffusion) propagates them over the network.…”

Section: Diffusion In Social Networkmentioning

confidence: 98%

Opinion Diffusion and Campaigning on Society Graphs

Faliszewski

Gonen

Koutecký

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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We study the effects of campaigning, where the society is partitioned into voter clusters and a diffusion process propagates opinions in a network connecting those clusters. Our model is very general and can incorporate many campaigning actions, various partitions of the society into voter clusters, and very general diffusion processes. Perhaps surprisingly, we show that computing the cheapest campaign for rigging a given election can usually be done efficiently, even with arbitrarily-many voters.

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“…Variations of this model consider the inclusion of persistent agents [17]. In [18] this update mechanism is used to formulate and solve an opinion manipulation problem. To account for more realistic social behaviour, the authors in [19] consider opinion dynamics where agents interact in pairs only when their opinions are already close.…”

Section: B Related Literaturementioning

confidence: 99%

Ranking Online Social Users by Their Influence

Giovanidis¹,

Baynat²,

Magnien³

et al. 2021

IEEE/ACM Trans. Networking

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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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Opinion Manipulation in Social Networks

Cited by 6 publications

References 12 publications

Performance Analysis of Online Social Platforms

Performance Analysis of Online Social Platforms

Opinion Diffusion and Campaigning on Society Graphs

Ranking Online Social Users by Their Influence

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