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This study provides an empirical and longitudinal account of how the South Korean podcast series Nakkomsu (NKS) employed a hybrid media strategy and served as an alternative political force challenging the ruling conservatives during three elections in South Korea between December 2011 and December 2012. The study is based on the methodological triangulation of hybrid web indicators resulting from a social network analysis, a semantic network analysis, and a link impact analysis, all conducted using big data mined from social media during the 13-month period. To examine the emergence and evolution of communication patterns around NKS on Twitter, all tweets that contained the Korean word for NKS and were publicly accessible were collected, amounting to a data set of 79,028 unique vertices and 1,866,085 edges. Our findings include the following. First, network density increased gradually over the period, reflecting a continuous decrease in the number of users participating in discussions around NKS. Second, according to quadratic assignment procedure (QAP) correlations, the semantic network characteristics of those discussions also changed over time, partly due to some systematic interference from members and supporters of the conservative party. Third, in this context Twitter served as a space where individual listeners interacted directly with the podcast and fellow listeners—a space that podcasts themselves do not provide. Fourth, the NKS phenomenon was characterized by the wide range of offline activities in which listeners were encouraged to participate alongside the podcast, such as books authored by panelists, public talks, and fundraising events. Such activities were marked not only by their critical content but also by elements of playfulness.

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This page is a summary of: Delineating the complex use of a political podcast in South Korea by hybrid web indicators: The case of the Nakkomsu Twitter network, Technological Forecasting and Social Change, September 2016, Elsevier,
DOI: 10.1016/j.techfore.2015.11.012.
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