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filtering procedures that only keep meaningful ties, where the traditional deviation-from-random-duplication filter has been replaced by alternatives such as backbone extraction.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.

Data availability statement

The data described in this article are openly available in the Open Science Framework at DOI:10.17605/OSF.IO/TPA6U.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Recent work has used statistical significance to determine which ties signify a theoretically meaningful organizing principle of the audience network. Which ties, however, count as statistically significant is – given the same probability threshold (e.g. p ≤ .05) – not only contingent on the number of media outlets’ joint users, but also depends on the sizes of the samples under study. Sample-size contingencies have been a central focus of the growing body of methodological research which has warned against conclusions on theoretical significance based on statistical significance (or lack thereof) (e.g. Sullivan & Feinn, 2012; Wasserstein, Schirm, & Lazar, 2019).

2. The general recommendations in the multilevel literature suggest that the accurate estimation of cross-national differences requires more than 10 news environments (Hox, 2010).

3. Density is the proportion of possible links in a network that are present. Degree centralization is the “degree to which the centrality of the most central point exceeds the centrality of all other points” (Freeman, 1978, p. 227). It summarizes the inequality among nodes’ degree centrality, as defined by the number of other nodes that a node is linked with. Both metrics fall into the range [0; 1]. Modularity scores fall into the range [−1; 1] and measure the division of the network into clusters (also called groups or communities) (see also Majó-Vázquez et al., 2019; Mukerjee et al., 2018a).

4. At least on theoretical grounds, many scholars assume that the overall concentration of audience attention on a media core of few mainstream outlets is, though varied, common to any modern news environment (Nelson & Webster, 2017; Webster & Ksiazek, 2012; Webster & Taneja, 2018).