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

Infodemiology and Infoveillance (1349) JMIR Theme Issue: COVID-19 Special Issue (2452) Early Results from COVID-19 Studies (299) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Information Quality in Digital Media (340) Vaccination and Immunization in the Digital Age (374) Infodemic Management (49)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49061, first published .
Exploring YouTube’s Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories

Exploring YouTube’s Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories

Exploring YouTube’s Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories

Authors of this article:

Yee Man Margaret Ng1, 2 Author Orcid Image ;   Katherine Hoffmann Pham1 Author Orcid Image ;   Miguel Luengo-Oroz1, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations (10) Metrics

Journals

  1. Pedreschi D, Pappalardo L, Ferragina E, Baeza-Yates R, Barabási A, Dignum F, Dignum V, Eliassi-Rad T, Giannotti F, Kertész J, Knott A, Ioannidis Y, Lukowicz P, Passarella A, Pentland A, Shawe-Taylor J, Vespignani A. Human-AI coevolution. Artificial Intelligence 2025;339:104244 View