Bipartite networks are a well known strategy to study a variety of phenomena. The commonly used method to deal with this type of network is to project the bipartite data into a unipartite weighted graph and then using a backboning technique to extract only the meaningful edges. Despite the wide availability of different methods both for projection and backboning, we believe that there has been little attention to the effect that the combination of these two processes has on the data and on the resulting network topology. In this paper we study the effect that the possible combinations of projection and backboning techniques have on a bipartite network. We show that the 12 methods group into two clusters producing unipartite networks with very different topologies. We also show that the resulting level of network centralization is highly affected by the combination of projection and backboning applied.
Among the many areas that have used bipartite data, the present paper was inspired by a recent study published in the area of communication research, more precisely in the sub-field of online audience studies @cite_24 . In this context, audience networks have been used for several years @cite_7 to study the general structure of audiences and answer key questions about echo-chambers, the existence of trend toward cyber-balkanization and filter bubbles. Audience networks are networks where nodes represents media sites that are connected if they share at least a part of their audience @cite_24 . While there are several strategies to collect audience behaviour, over the recent years the common practice has been the use of trace data -- digital traces left behind by users browsing the web @cite_24 . This is also due to intrinsic limitation of self-reported data @cite_10 .
@cite_24 @cite_10 @cite_7
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Measures of audience overlap between news sources give us information on the diversity of people’s media diets and the similarity of news outlets in terms of the audiences they share. This provides... Many studies of media effects use self-reported news exposure as their key independent variable without establishing its validity. Motivated by anecdotal evidence that people's reports of their own media use can differ considerably from independent assessments, this study examines systematically the accuracy of survey-based self-reports of news exposure. I compare survey estimates to Nielsen estimates, which do not rely on self-reports. Results show severe overreporting of news exposure. Survey estimates of network news exposure follow trends in Nielsen ratings relatively well, but exaggerate exposure by a factor of 3 on average and as much as eightfold for some demographics. It follows that apparent media effects may arise not because of differences in exposure, but because of unknown differences in the accuracy of reporting exposure. This article explains and implements a network analytic approach to the study of cross-platform audience behavior. It begins by conceptualizing large-scale patterns of media use in network terms, treating media outlets as nodes and the levels of audience duplication among them as links. Following that, it explains 2 common measures of audience duplication, Absolute Duplication and Primary Duplication, and offers a new measure, Deviation-from-Random Duplication. In doing so, techniques for converting duplication data into network data are discussed. This approach is then applied to analyze patterns of audience fragmentation, media publics, and audience polarization using data from Nielsen's TV Internet Convergence Panel. The findings show the value of using a network approach, by contributing to an alternative understanding of these patterns. Economic and policy implications are discussed, as well as broader reflections on the use of network analysis in the study of audience behavior.