A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks.

ABSTRACT: The use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection algorithms are the best suited for studying these dynamics. In this paper, I address this question by proposing a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the leve

SUBMITTER: Mukerjee S 

PROVIDER: S-EPMC8313591 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature