ArticleInternational journal of environmental research and public health2021
Cultural Variance in Reception and Interpretation of Social Media COVID-19 Disinformation in French-Speaking Regions.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- From emergence to amplification: an analysis of lifecycle models to address health mis-disinformation in the digital environment.Frontiers in medicine · 2026Review
- Vaccine Confidence During Public Health Challenges and Prior to HPV Vaccine Introduction in Mali.Vaccines · 2025Article
- Conspiracy narratives and vaccine hesitancy: a scoping review of prevalence, impact, and interventions.BMC public health · 2024Article
- Online Information of COVID-19: Visibility and Characterization of Highest Positioned Websites by Google between March and April 2020-A Cross-Country Analysis.International journal of environmental research and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital communication technology has created a world in which media are capable of crossing national boundaries as never before. As a result, language is increasingly the salient category determining individuals' media consumption. Today, a single social media post can travel around the world, reaching anyone who speaks its language. This poses significant challenges to combatting the spread of disinformation, as an ever-growing pool of disinformation purveyors reach audiences larger than ever before. This dynamic is complicated, however, by the diversity of audience interpretations of message content within a particular language group. Both across and within national boundaries, a single message may be subject to a variety of interpretations depending on the cultural experiences and identities of its recipients. This study explores that dynamic through analysis of French language anti-vaccine and COVID-denialist disinformation. Using qualitative coding methodology, a team of researchers empirically identify common and far-reaching patterns of Francophone COVID disinformation narratives and rhetoric. These narratives and rhetorics are then subjected to hermeneutic close reading to determine likely variations in their reception across different French-speaking cultures. Data were gathered and analyzed between the dates of 24 March 2021 and 27 April 2021. Results of this study indicate the need for awareness on the part of public health officials combatting COVID disinformation online, for both the transnational reach of disinformation targeting speakers of a single language and for variations in meaning and salience across cultures within that language group.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.