A Computational Approach to Analyzing the Twitter Debate on Gaming Disorder

Open Access Journal | ISSN: 2183-2439

Article | Open Access

A Computational Approach to Analyzing the Twitter Debate on Gaming Disorder


  • 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


Full Text   PDF (free download)
Views: 1501 | Downloads: 1662


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.

Keywords:  addiction; content analysis; entertainment research; games; gaming disorder; social media

Published:   13 August 2020


Supplementary Files:

DOI: https://doi.org/10.17645/mac.v8i3.3128


© Tim Schatto-Eckrodt, Robin Janzik, Felix Reer, Svenja Boberg, Thorsten Quandt. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0), which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.