Measuring Selective Exposure: A Systematic Comparison of Community Detection Algorithms in Coexposure Networks
The use of network analytic techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. In this paper, I propose 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 level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. Finally, I validate these findings using a novel empirical data-set of actual large-scale browsing behavior and demonstrate the model's utility in informing future analytical choices.