Polarización y similitud en las representaciones de mensajes oficiales en medios ante la pandemia de COVID-19

By using a triple machine learning approach, we conduct a content analysis on dos million bites of Mexican government information, and 28, 127 news and opinion columns published in 2020, relating to COVID pandemia. Findings report similarity in news and polarization in opinion columns. This means th...

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Bibliographic Details
Main Authors: Huerta Wong, Juan Enrique, Castañeda Valencia, Alejandro Miguel, Manzano Mora, Francisco Javier
Format: Article
Language:Spanish
Published: 2023
Subjects:
COVID-19
media
communication and media
mass media studies
methods
machine learning
information polarization
medios de comunicación
comunicación y medios
estudios de medios de comunicación
métodos
aprendizaje máquina
polarización de la información
Online Access:https://dialnet.unirioja.es/servlet/oaiart?codigo=8913367
Source:Estudios en derecho a la información, ISSN 2683-2038, Nº. 16 (Julio-diciembre 2023), 202377 pags.
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Polarización y similitud en las representaciones de mensajes oficiales en medios ante la pandemia de COVID-19
Summary: By using a triple machine learning approach, we conduct a content analysis on dos million bites of Mexican government information, and 28, 127 news and opinion columns published in 2020, relating to COVID pandemia. Findings report similarity in news and polarization in opinion columns. This means that news covered accurately Health General Council recommendations, but comments in media tended to distort what this group of professionals in Medicine provided with the objective of protect Mexicans’ lives. Those findings bring media social responsibility and right to information to the further discussions of COVID-19, the most serious health crisis in Mexico history.

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