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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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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.

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Fig. 1

Similar content being viewed by others

Notes

  1. The terms such as “structural similarity,” “structural variable,” “structural quality,” and the like that appear throughout the manuscript are used as is common in the audience behavior literature, not as they are used in the context of network analysis. In other words, these terms have no relation to network structure or network pattern. Rather, they refer to aspects of viewer availability such as multichannel subscription and time spent viewing television, consistent with the studies by Webster (2011) and Taneja et al. (2012, p. 954).

  2. The data were retrieved from the homepage of the Ministry of Science, ICT, and Future Planning: http://www.msip.go.kr/www/brd/m_220/list.do.

  3. The data in this sentence were retrieved from the homepage of the Korea Communications Commission: http://www.kcc.go.kr/tsi/etc/search/search/ASC_integrationsearch.jsp?page=A10010000.

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Acknowledgement

This research was funded by the Republic of Korea’s Foundation for Broadcast Culture (http://www.fbc.or.kr) in 2014. In addition, the Institutional Review Board (IRB) at Kookmin University, Republic of Korea approved this study as exempt from review (KMU-201406-HR-023).

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Correspondence to Sujin Choi.

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Choi, S. How do sociodemographic and structural similarities explain viewing pattern similarity by channel type? Insight from a network analytic approach. Qual Quant 51, 1093–1112 (2017). https://doi.org/10.1007/s11135-016-0461-8

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