How Do Filtering Choices Impact the Structures of Audience Networks? A Simulation Study Using Data from 26 Countries

Abstract

Although communication scholars have increasingly studied audience fragmentation by analyzing audience networks, recent cross-national research has spurred a methodological debate about how to construct these audience networks from media use data. One point of contention lies in the choice of filtering procedures that only keep meaningful ties, where the traditional deviation-from-random-duplication filter has been replaced by alternatives such as backbone extraction. Using survey data from the 2016 Reuters Institute Digital News Report on news usage in 26 countries, we examined how filtering choices impact the structures of audience networks through a resampling simulation experiment. The simulation revealed a strong divergence between the filtering approaches, which invited disparate interpretations regarding the fragmentation of modern news audiences. The backbone extraction approach produced the most distinct results, while dealing most effectively with randomness in the media use data. As the filtering approaches mapped the structural features of audiences to different values of the same network metrics, the findings imply that studies diverging in these analytical decisions do not produce compatible results.