#6270. The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic

September 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Communication;
Computer Networks and Communications;
Information Systems;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №6270.1 Contract №6270.2 Contract №6270.3 Contract №6270.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
During the coronavirus disease 20XX (COVID-19) pandemic, the video-sharing platform YouTube has been serving as an essential instrument to widely distribute news related to the global public health crisis and to allow users to discuss the news with each other in the comment sections. Along with these enhanced opportunities of technology-based communication, there is an overabundance of information and, in many cases, misinformation about current events. In times of a pandemic, the spread of misinformation can have direct detrimental effects, potentially influencing citizens behavioral decisions (e.g., to not socially distance) and putting collective health at risk. Misinformation could be especially harmful if it is distributed in isolated news cocoons that homogeneously provide misinformation in the absence of corrections or mere accurate information. The present study analyzes data gathered at the beginning of the pandemic (January–March 20XX) and focuses on the network structure of YouTube videos and their comments to understand the level of informational homogeneity associated with misinformation on COVID-19 and its evolution over time. This study combined machine learning and network analytic approaches.
Keywords:
COVID-19; Deep Learning, Social Media; Homogeneity; Infodemic; Misinformation; Network Analysis; YouTube

Contacts :
0