Topic Analysis During Public Health Crises
Media Coverage of COVID-19 Vaccines in Mexico
DOI:
https://doi.org/10.20318/recs.2026.9693Keywords:
Pandemics, COVID-19, News, Natural Language ProcessingAbstract
Introduction: This study examines the evolution of thematic frameworks in Mexican media coverage of COVID-19 vac cines between 2020 and 2021, given that media narratives shape public perception and institutional trust during health crises. Objectives: The research aims to identify and analyze the dominant themes in COVID-19 vaccine coverage in Mexico and track their temporal evolution. Methodology: A corpus of 43.358 articles from major national newspapers in Mexico was analyzed. Using Latent Dirichlet Allocation (LDA), a topic modeling technique from Natural Language Processing, news content was categorized quarterly to map thematic developments over time. Results: Five key topics were identified: global health and economic crisis, international relations, pandemic management at the state level, institutional responses to social groups, and national public health policy. Findings show a clear progression from global crisis frames to more localized policy discussions. Conclusion: The study highlights how media coverage evolves in response to social and epidemiological changes, providing insights for designing timely, culturally appropriate health communication strate gies.
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