Digital Media Engagement and Health Communication: Analyzing YouTube Reactions to a Brain Dead Pregnant Woman Case

Public discourse and emotional engagement on YouTube surrounding a controversial case of a brain-dead pregnant woman maintained on life support to sustain fetal development are examined in this study. A mixed methods design was used to analyze the comments on YouTube videos related to the case of a brain-dead pregnant woman kept on life support to allow the fetus to live. Machine driven classification of open responses (MDCOR) was used to identify how users constructed their comments around discursive frames and sentiment and affective network analysis (SENA) was used to determine if there were any differences in sentiment across the videos from different sources. Additionally, the study explored how health literacy emerged as a thematic pattern. The analysis revealed that the public’s responses to this controversial issue constructed different types of responses reflecting different attitudes and emotions towards the case. These in turn revealed differences in health literacy and in access to credible health information sources.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-02-13
DOI
https://doi.org/10.1051/shsconf/202622902004/pdf
Primary Topic
Health Literacy and Information Accessibility
Type
article
Field-Weighted Citation Impact
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article

Digital Media Engagement and Health Communication: Analyzing YouTube Reactions to a Brain Dead Pregnant Woman Case

Yalan Chen
Springer Link (Chiba Institute of Technology)
Health Literacy and Information Accessibility
article

Digital Media Engagement and Health Communication: Analyzing YouTube Reactions to a Brain Dead Pregnant Woman Case

Yalan Chen
article en

Abstract

Public discourse and emotional engagement on YouTube surrounding a controversial case of a brain-dead pregnant woman maintained on life support to sustain fetal development are examined in this study. A mixed methods design was used to analyze the comments on YouTube videos related to the case of a brain-dead pregnant woman kept on life support to allow the fetus to live. Machine driven classification of open responses (MDCOR) was used to identify how users constructed their comments around discursive frames and sentiment and affective network analysis (SENA) was used to determine if there were any differences in sentiment across the videos from different sources. Additionally, the study explored how health literacy emerged as a thematic pattern. The analysis revealed that the public’s responses to this controversial issue constructed different types of responses reflecting different attitudes and emotions towards the case. These in turn revealed differences in health literacy and in access to credible health information sources.

Springer Link (Chiba Institute of Technology)
Quality Education
Openalex Percentile: Top 58%
Health Literacy and Information Accessibility
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