Information Communities in Social Networks. Part III: Applied Aspects of Detection and Analysis

Abstract. This paper overviews the empirical studies of the formation and detection of information communities in social networks. In parts I and II of the survey, we outlined the concept of an information community and considered the relevant mathematical models describing the formation of private beliefs. Model identification, data gathering, and data analysis become highlighted areas of current research due to the uncertainty about social learning mechanisms and networked interaction structure. To solve the identification problem, researchers carry out behavioral experiments and field investigations. In practice, researchers analyze communities on available real-world data, applying methods based on the structural properties of the network of information interactions between agents, the individual characteristics of agents, and a combination of structural and individual characteristics. Part III of the survey presents studies on identifying learning models and discusses some practical aspects of analyzing information communities in social networks.

Keywords: social networks, information community, formation of information communities, belief formation, identification of information communities.

Funding. This research was supported by the Russian Foundation for Basic Research, projects nos. 19-17-50225 and 20-07-00190 (Gubanov D.A.), and 20-31-90133 (Petrov I.V.).

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Boiko, L.M., Gubanov, D.A., Petrov, I.V. Information Communities in Social Networks. Part III: Applied Aspects of Detection and Analysis. Control Sciences 3, 14–21 (2021).http://doi.org/10.25728/cs.2021.3.2