Content analysis

Health and science-related disinformation on COVID-19: A content analysis of hoaxes identified by fact-checkers in Spain

This article was originally published here

PLoS One. 2022 Apr 13;17(4):e0265995. doi: 10.1371/journal.pone.0265995. eCollection 2022.


A massive “infodemic” has developed alongside the global COVID-19 pandemic and contributed to public misinformation at a time when access to quality information was crucial. This research aimed to analyze the science and health-related hoaxes that have spread during the pandemic with the aim of (1) identifying the characteristics of the form and content of this false information, and the platforms used to spread it , and (2) formulate a typology to classify the different types of hoaxes according to their link with scientific information. The study was conducted by analyzing the content of hoaxes that were debunked by the three main fact-checking organizations in Spain within three months of the WHO pandemic announcement (N=533). The results indicated that science and health content played a prominent role in shaping the spread of these hoaxes during the pandemic. The most common science and health hoaxes involved scientific research or health management information, used text, were based on deception, used real sources, were international in scope, and spread via social media. social. Based on the analysis, we proposed a classification system for science and health-related hoaxes, and identified four types based on their connection to scientific knowledge: “hasty” science, decontextualized science, misinterpreted science and unscientific lying. The rampant spread and widespread availability of misinformation underscores the need to encourage media and scientific caution and literacy among the public and to increase awareness of the importance of the timing and justification of scientific research. The results may be useful for improving media literacy to deal with misinformation, and the typology we formulate may help develop future systems for automated detection of health and science-related hoaxes.

PMID:35417493 | DOI:10.1371/journal.pone.0265995