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Politicizing for the idol: China’s idol fandom nationalism in pandemic

Author

Listed:
  • Wang, Yan
  • Luo, Ting

Abstract

Chinese idol fans have been identified among the main forces in cyber nationalist activisms in recent years, acting as the nationalist fans protecting the state as an idol in response to external political shocks. Their skills in acknowledging, involving, and even reinventing the image of the state and national pride in cyber nationalist activisms do not emerge in a vacuum. This article examines how idol fans involve and reinvent the nationalist discourse in their everyday fan activities–idol promotion. We focus on the pandemic in 2020 as it provides a specific social and political context that allows us to understand better the interaction between idol fans and the state in their mundane fan activities. We construct our analysis under the computational grounded theory framework with over 6 million fan posts collected from Weibo and 11 in-depth interviews with active idol fans. Our findings show that when engaging in pandemic-related discussion, idol fans actively borrowed official discourse on nationalism and strategically responded to key political and social events in their idol promotion activities. The idol images they built are not only positive but also nationalist. Therefore, they play not only the commercial logic commonly seen in the Japanese and Korean K-pop/idol culture but also the political logic propagated by the state in China.

Suggested Citation

  • Wang, Yan & Luo, Ting, 2023. "Politicizing for the idol: China’s idol fandom nationalism in pandemic," LSE Research Online Documents on Economics 117741, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:117741
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    File URL: https://researchonline.lse.ac.uk/id/eprint/117741/
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    References listed on IDEAS

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    1. Laura K. Nelson, 2020. "Computational Grounded Theory: A Methodological Framework," Sociological Methods & Research, , vol. 49(1), pages 3-42, February.
    2. Denny, Matthew J. & Spirling, Arthur, 2018. "Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It," Political Analysis, Cambridge University Press, vol. 26(2), pages 168-189, April.
    3. repec:gig:chaktu:v:44:y:2015:i:2:p:69-103 is not listed on IDEAS
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    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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