Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis
BackgroundA worldwide overabundance of information comprising misinformation, rumors, and propaganda concerning COVID-19 has been observed in addition to the pandemic. By addressing this data confusion, Wikipedia has become an important source of information. Obje...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e45024 |
| _version_ | 1797733989210914816 |
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| author | Kunhao Yang Mikihito Tanaka |
| author_facet | Kunhao Yang Mikihito Tanaka |
| author_sort | Kunhao Yang |
| collection | DOAJ |
| description |
BackgroundA worldwide overabundance of information comprising misinformation, rumors, and propaganda concerning COVID-19 has been observed in addition to the pandemic. By addressing this data confusion, Wikipedia has become an important source of information.
ObjectiveThis study aimed to investigate how the editors of Wikipedia have handled COVID-19–related information. Specifically, it focused on 2 questions: What were the knowledge preferences of the editors who participated in producing COVID-19–related information? and How did editors with different knowledge preferences collaborate?
MethodsThis study used a large-scale data set, including >2 million edits in the histories of 1857 editors who edited 133 articles related to COVID-19 on Japanese Wikipedia. Machine learning methods, including graph neural network methods, Bayesian inference, and Granger causality analysis, were used to establish the editors’ topic proclivity and collaboration patterns.
ResultsOverall, 3 trends were observed. Two groups of editors were involved in the production of information on COVID-19. One group had a strong preference for sociopolitical topics (social-political group), and the other group strongly preferred scientific and medical topics (scientific-medical group). The social-political group played a central role (contributing 16,544,495/23,485,683, 70.04% of bits of content and 57,969/76,673, 75.61% of the references) in the information production part of the COVID-19 articles on Wikipedia, whereas the scientific-medical group played only a secondary role. The severity of the pandemic in Japan activated the editing behaviors of the social-political group, leading them to contribute more to COVID-19 information production on Wikipedia while simultaneously deactivating the editing behaviors of the scientific-medical group, resulting in their less contribution to COVID-19 information production on Wikipedia (Pearson correlation coefficient=0.231; P<.001).
ConclusionsThe results of this study showed that lay experts (ie, Wikipedia editors) in the fields of science and medicine tended to remain silent when facing high scientific uncertainty related to the pandemic. Considering the high quality of the COVID-19–related articles on Japanese Wikipedia, this research also suggested that the sidelining of the science and medicine editors in discussions is not necessarily a problem. Instead, the social and political context of the issues with high scientific uncertainty is more important than the scientific discussions that support accuracy. |
| first_indexed | 2024-03-12T12:36:37Z |
| format | Article |
| id | doaj.art-cb20ccdfc61c477d957a1ffc99229fcc |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:36:37Z |
| publishDate | 2023-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-cb20ccdfc61c477d957a1ffc99229fcc2023-08-29T00:08:36ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-06-0125e4502410.2196/45024Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical AnalysisKunhao Yanghttps://orcid.org/0000-0002-7654-9441Mikihito Tanakahttps://orcid.org/0000-0002-9001-0805 BackgroundA worldwide overabundance of information comprising misinformation, rumors, and propaganda concerning COVID-19 has been observed in addition to the pandemic. By addressing this data confusion, Wikipedia has become an important source of information. ObjectiveThis study aimed to investigate how the editors of Wikipedia have handled COVID-19–related information. Specifically, it focused on 2 questions: What were the knowledge preferences of the editors who participated in producing COVID-19–related information? and How did editors with different knowledge preferences collaborate? MethodsThis study used a large-scale data set, including >2 million edits in the histories of 1857 editors who edited 133 articles related to COVID-19 on Japanese Wikipedia. Machine learning methods, including graph neural network methods, Bayesian inference, and Granger causality analysis, were used to establish the editors’ topic proclivity and collaboration patterns. ResultsOverall, 3 trends were observed. Two groups of editors were involved in the production of information on COVID-19. One group had a strong preference for sociopolitical topics (social-political group), and the other group strongly preferred scientific and medical topics (scientific-medical group). The social-political group played a central role (contributing 16,544,495/23,485,683, 70.04% of bits of content and 57,969/76,673, 75.61% of the references) in the information production part of the COVID-19 articles on Wikipedia, whereas the scientific-medical group played only a secondary role. The severity of the pandemic in Japan activated the editing behaviors of the social-political group, leading them to contribute more to COVID-19 information production on Wikipedia while simultaneously deactivating the editing behaviors of the scientific-medical group, resulting in their less contribution to COVID-19 information production on Wikipedia (Pearson correlation coefficient=0.231; P<.001). ConclusionsThe results of this study showed that lay experts (ie, Wikipedia editors) in the fields of science and medicine tended to remain silent when facing high scientific uncertainty related to the pandemic. Considering the high quality of the COVID-19–related articles on Japanese Wikipedia, this research also suggested that the sidelining of the science and medicine editors in discussions is not necessarily a problem. Instead, the social and political context of the issues with high scientific uncertainty is more important than the scientific discussions that support accuracy.https://www.jmir.org/2023/1/e45024 |
| spellingShingle | Kunhao Yang Mikihito Tanaka Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis Journal of Medical Internet Research |
| title | Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis |
| title_full | Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis |
| title_fullStr | Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis |
| title_full_unstemmed | Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis |
| title_short | Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis |
| title_sort | crowdsourcing knowledge production of covid 19 information on japanese wikipedia in the face of uncertainty empirical analysis |
| url | https://www.jmir.org/2023/1/e45024 |
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