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A Method for Identifying Bridges in Online Social Networks

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1573)

Abstract

The current level of development of online social networks has transformed social media from a way of communication between people into a tool for influencing people’s behaviour in their daily lives. This influence is often aimed at inciting protest movements in society and mobilising citizens for protest actions, and has a targeted impact on social network users. The sponsors and main actors of disruptive influences are often forces located in other countries. In the context of counteraction to targeted destructive influences, the task of identifying the network structure of destructive influence is very relevant. One element of this structure is the users connecting individual communities to the core of the protest network. These users are the bridges between the clusters and the core network. Their main task is to contribute to the rapid growth of the protest audience. Identifying the most influential bridges and blocking them could decrease the protest potential or make the protest actions ineffective. In this paper, we propose a methodology for identifying bridge users based on the original centrality measure of weighted contribution. Moreover, a method for identifying the most influential bridges is proposed. Unlike most probabilistic methods, weighted contribution centrality allows for clear determination of whether a user is a bridge or not. A description of the measure, a mathematical model and an algorithm for calculating it are presented.

Keywords

  • Online social networks
  • Social network analysis
  • Structure of protest network
  • Core of protest network
  • Clusters
  • Community
  • Bridges
  • Weighted contribution centrality

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Correspondence to Andrey N. Rabchevskiy .

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Rabchevskiy, A.N., Zayakin, V.S., Rabchevskiy, E.A. (2022). A Method for Identifying Bridges in Online Social Networks. In: , et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2021. Communications in Computer and Information Science, vol 1573. Springer, Cham. https://doi.org/10.1007/978-3-031-15168-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-15168-2_14

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