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 (1833) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1104) Information Quality in Digital Media (386) Infodemiology and Infoveillance (1496) Quality/Credibility of eHealth Information and Trust Issues (624) 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 (3) Tweetations (54) 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

Conference Proceedings

  1. Yang K, Fu M. 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). Exploring Divergence in Collective Perception to Noto Earthquake Through News Comment Data View