Silence in the Stands: Assessing the Impact of Russian State‐Linked "Sportswashing" on Online Fan Behavior Following the Full‐Scale Invasion of Ukraine.

  • Published In: Social Science Quarterly (Wiley-Blackwell), 2025, v. 106, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wack, Morgan; Magistro, Beatrice; Aslett, Kevin 3 of 3

Abstract

Objective: This study examines whether state‐linked sportswashing—through perceived associations between states and sports entities—can shield states from public scrutiny over human rights abuses. We analyze changes in Chelsea F.C. supporters' online behavior following Roman Abramovich's announcement to relinquish control of the club during Russia's invasion of Ukraine. Method: We collected over 700,000 tweets from 7414 profiles of London‐based English Premier League fans. Using a fine‐tuned BERT machine learning model, we classified tweets mentioning the invasion, criticizing Russia, or supporting Ukraine. We applied a generalized synthetic control method to compare Chelsea fans' online behavior to that of other clubs' fans before and after the announcement. Results: After the announcement, Chelsea fans were less likely than other fans to discuss the invasion, support Ukraine, or criticize Russia. Conclusion: The findings suggest that perceptions of state‐linked associations can influence supporter online behavior via social identity mechanisms, potentially mitigating criticism of state actions. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Science Quarterly (Wiley-Blackwell). 2025/01, Vol. 106, Issue 1, p1
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2025
  • ISSN:0038-4941
  • DOI:10.1111/ssqu.13485
  • Accession Number:183867462
  • Copyright Statement:Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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