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A Computational Approach to Analyzing the Twitter Debate on Gaming Disorder

Author

Listed:
  • Tim Schatto-Eckrodt

    (University of Münster, Department of Communication, Germany)

  • Robin Janzik

    (University of Münster, Department of Communication, Germany)

  • Felix Reer

    (University of Münster, Department of Communication, Germany)

  • Svenja Boberg

    (University of Münster, Department of Communication, Germany)

  • Thorsten Quandt

    (University of Münster, Department of Communication, Germany)

Abstract

The recognition of excessive forms of media entertainment use (such as uncontrolled video gaming or the use of social networking sites) as a disorder is a topic widely discussed among scientists and therapists, but also among politicians, journalists, users, and the industry. In 2018, when the World Health Organization (WHO) decided to include the addictive use of digital games (gaming disorder) as a diagnosis in the International Classification of Diseases, the debate reached a new peak. In the current article, we aim to provide insights into the public debate on gaming disorder by examining data from Twitter for 11 months prior to and 8 months after the WHO decision, analyzing the (change in) topics, actors, and sentiment over time. Automated content analysis revealed that the debate is organic and not driven by spam accounts or other overly active ‘power users.’ The WHO announcement had a major impact on the debate, moving it away from the topics of parenting and child welfare, largely by activating actors from gaming culture. The WHO decision also resulted in a major backlash, increasing negative sentiments within the debate.

Suggested Citation

  • Tim Schatto-Eckrodt & Robin Janzik & Felix Reer & Svenja Boberg & Thorsten Quandt, 2020. "A Computational Approach to Analyzing the Twitter Debate on Gaming Disorder," Media and Communication, Cogitatio Press, vol. 8(3), pages 205-218.
    • Handle: RePEc:cog:meanco:v:8:y:2020:i:3:p:205-218
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    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. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
    4. Natalia Levina & Manuel Arriaga, 2014. "Distinction and Status Production on User-Generated Content Platforms: Using Bourdieu’s Theory of Cultural Production to Understand Social Dynamics in Online Fields," Information Systems Research, INFORMS, vol. 25(3), pages 468-488, September.
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    Cited by:

    1. Johannes Breuer & Tim Wulf & M. Rohangis Mohseni, 2020. "New Formats, New Methods: Computational Approaches as a Way Forward for Media Entertainment Research," Media and Communication, Cogitatio Press, vol. 8(3), pages 147-152.

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