Trend analysis of COVID-19 mis/disinformation narratives-A 3-year study.

ABSTRACT: To tackle the COVID-19 infodemic, we analysed 58,625 articles from 460 unverified sources, that is, sources that were indicated by fact checkers and other mis/disinformation experts as frequently spreading mis/disinformation, covering the period from 1 January 2020 to 31 December 2022. Our aim was to identify the main narratives of COVID-19 mis/disinformation, develop a codebook, automate the process of narrative classification by training an automatic classifier, and analyse the spread of narratives over time and across countries. Articles were retrieved with a customised version of the Europe Media Monitor (EMM) processing chain providing a stream of text items. Machine translation was employed to automatically translate non-English text to English and clustering was carried out to group

SUBMITTER: Kotseva B 

PROVIDER: S-EPMC10655972 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature