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How do sociodemographic and structural similarities explain viewing pattern similarity by channel type? Insight from a network analytic approach

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  • Sujin Choi

    (Kookmin University)

Abstract

This study explores audience polarization through a finer-grained approach by using network analysis of people-meter data. We examine how sociodemographic and structural similarities between viewers contribute to similarities in their television viewing patterns in general and by channel type. This study provides a systematic and comprehensive account of audience behavior by integrating sociodemographics, structural factors, and content types that have rarely been examined in a single integrated model. The findings suggest that structural similarities continue to be important even in the highly selective media environment. Although individuals’ latitudes of choice have increased with the multitude of channel options, choices are not scattered over hundreds of channels based on sociodemographic attributes. Certain channels are viewed almost solely by individuals in a specific demographic category; however, many other channels have viewers across gender, age, and occupational categories. De facto polarization due to the number of channels included in viewers’ subscriptions leads to actual polarization only for entertainment content. The findings relate to a broader thesis on the flow of individuals’ attention to certain types of content and the long-term concerns about the creation of cocoons of self-selected content. This study demonstrates how network analysis can contribute to examining audience behavior.

Suggested Citation

  • Sujin Choi, 2017. "How do sociodemographic and structural similarities explain viewing pattern similarity by channel type? Insight from a network analytic approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1093-1112, May.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0461-8
    DOI: 10.1007/s11135-016-0461-8
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    References listed on IDEAS

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    1. Lanham, Richard A., 2006. "The Economics of Attention," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226468822, September.
    2. Markus Prior, 2005. "News vs. Entertainment: How Increasing Media Choice Widens Gaps in Political Knowledge and Turnout," American Journal of Political Science, John Wiley & Sons, vol. 49(3), pages 577-592, July.
    3. Thomas B. Ksiazek, 2011. "A Network Analytic Approach to Understanding Cross-Platform Audience Behavior," Journal of Media Economics, Taylor & Francis Journals, vol. 24(4), pages 237-251, November.
    4. David Dekker & David Krackhardt & Tom Snijders, 2007. "Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 563-581, December.
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    Cited by:

    1. Oriol Barranco & Carlos Lozares & Dafne Muntanyola-Saura, 2019. "Heterophily in social groups formation: a social network analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 599-619, March.

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