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Understanding Political Polarization Based on User Activity: A Case Study in Korean Political YouTube Channels

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  • Giang T. C. Tran
  • Luong Vuong Nguyen
  • Jason J. Jung
  • Jeonghun Han

Abstract

This study proposes a novel approach for measuring political polarization using a user-activity-based model. By exploiting data from comments, user activity in this study is defined based on features such as coverage, duration, and enthusiasm. To determine these features, we collect information on the activities of users from South Korean YouTube channels. Notably, the collected data of the model contains approximately 11 M comments from more than 600 K users based on 37 K videos of 77 YouTube channels. To handle the big data collection, we deploy a web-based platform called TubePlunger to collect video information (e.g., comments, replies, etc.) automatically from YouTube channels. The output of the model reveals that the users are strongly polarized because the number of neutral users is very small (approximately 8% of the total). We then applied this model to the other channels in the testing dataset to define polarization with a bias percentage and to visualize the user activity distribution. The experimental results show that there are 30 fully polarized YouTube channels (16 left-wing channels and 14 right-wing channels) with a measured bias ratio higher than 70%. Our method of analyzing social network data based on user activity provides the foundation for polarization analysis that can be applied to fields other than politics.

Suggested Citation

  • Giang T. C. Tran & Luong Vuong Nguyen & Jason J. Jung & Jeonghun Han, 2022. "Understanding Political Polarization Based on User Activity: A Case Study in Korean Political YouTube Channels," SAGE Open, , vol. 12(2), pages 21582440221, April.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221094587
    DOI: 10.1177/21582440221094587
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    References listed on IDEAS

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    1. Matthew S. Levendusky, 2013. "Why Do Partisan Media Polarize Viewers?," American Journal of Political Science, John Wiley & Sons, vol. 57(3), pages 611-623, July.
    2. Luong Vuong Nguyen & Jason J. Jung, 2020. "Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Reliability and Statistical Computing, pages 139-150, Springer.
    3. Tim Groseclose & Jeffrey Milyo, 2005. "A Measure of Media Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(4), pages 1191-1237.
    4. David Strömberg, 2004. "Mass Media Competition, Political Competition, and Public Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(1), pages 265-284.
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    Cited by:

    1. Seungwoo Han, 2024. "Analyzing “Jayu” in South Korean presidential rhetoric: a comprehensive study from 1948–2023 with a focus on the Yoon Suk Yeol administration," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-25, December.

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