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 similar articles. A multi-level codebook of COVID-19 mis/disinformation narratives was developed following an inductive approach; a transformer-based model was developed to classify all text items according to the codebook. Using the transformer-based model, we identified 12 supernarratives that evolved over the three years studied. The analysis shows that there are often real events behind mis/disinformation trends, which unverified sources misrepresent or take out of context. We established a process that allows for near real-time monitoring of COVID-19 mis/disinformation. This experience will be useful to analyse mis/disinformation about other topics, such as climate change, migration, and geopolitical developments.

Development of a roadmap for action on Evidence‐based risk communication in the EU Food Safety System

“…The interviews and mapping of current initiatives by organisations in the identification of false information found that false information tends to be clustered within social media communities. Another relevant finding was that the use of Artificial Intelligence (AI) shows promise in supporting the identification of false information, as evidenced by the successful AI false information identification system developed by DG JRC (Kotseva et al, 2023). The interviews also revealed that there is a high willingness among institutions to collaborate with EFSA in identifying false information within the scope of this project.…”

Subjective health literacy among Polish adolescents in 2018 vs 2022 – impact of gender, age, and socio-economic factors during COVID-19

“…Interpreting these findings would necessitate in-depth analysis, especially to explore the potential factors influencing HL during this period. The diminished self-perception of health knowledge and awareness could at least, in part, stem from widespread misinformation/disinformation in the media and adult discussions during the pandemic [32]. An Australian study has reported a correlation between susceptibility to misinformation and lower health literacy levels [33].…”