This paper is in the following e-collection/theme issue:

Theme Issue 2022: The Present and Future of Pandemic Technologies (11) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1759) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1081) Information Quality in Digital Media (364) Infodemiology and Infoveillance (1434) Quality/Credibility of eHealth Information and Trust Issues (586) Wikis (17)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45024, first published .
Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis

Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis

Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis

Authors of this article:

Kunhao Yang1 Author Orcid Image ;   Mikihito Tanaka2 Author Orcid Image
Article Authors Cited by (2) Tweetations (53) Metrics

Journals

  1. Segundo R, Magaly D, Luis C, Otiniano N, Soto-Deza N, Terrones-Rodriguez N, Mayra D. Bibliometric Analysis: Use of Agricultural Waste in the Generation of Electrical Energy. Processes 2024;12(6):1178 View
  2. Yang K, Fu M. Unsupervised Learning Methods Reveal the Complexity of User Segregation in Political Sphere in Online Communities. Procedia Computer Science 2024;246:538 View