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Related Experiment Videos

Analyzing Misinformation and Disinformation: Understanding Swiss COVID-19 Narratives Through Natural Language

Federico Germani¹, Giovanni Spitale¹, Franc Fritschi²

¹Institute of Biomedical Ethics and History of Medicine, University of Zurich, Winterthurerstrasse 30, Zürich, 8006, Switzerland, 41 44 634 40 80.

JMIR Infodemiology

|March 30, 2026

Summary

This summary is machine-generated.

Misinformation and disinformation framing differs between Swiss news media and social media during COVID-19. Promoting information literacy is key to building resilience and reducing societal polarization.

Keywords:

COVID-19 Switzerland disinformation misinformation natural language processing public discourse

Related Experiment Videos

Area of Science:

Social Sciences
Communication Studies
Public Health

Background:

The COVID-19 pandemic amplified challenges from misinformation and disinformation, increasing polarization and distrust.
Understanding public discourse framing of these terms is crucial for crisis resilience and informed decision-making.

Purpose of the Study:

To explore the framing of misinformation and disinformation in Swiss public discourse during the COVID-19 pandemic.
To inform strategies for mitigating societal impacts by promoting a common understanding of these terms.

Main Methods:

Analysis of two datasets: newspaper articles (Factiva) and social media posts (CrowdTangle).
Utilized natural language processing: lemmatization, co-occurrence analysis, and semantic network mapping.

Main Results:

News media framed misinformation/disinformation around public sentiment and journalistic integrity.
Social media displayed polarized narratives, conspiracy theories, and institutional distrust.

Conclusions:

Diverging narratives on misinformation/disinformation reflect societal tensions.
Journalistic integrity and information literacy are vital for bridging divides and reducing polarization.