Analyzing the Overturn of Roe v. Wade: A Term Co-Occurrence Network Analysis of YouTube Comments

Objective: This study examines YouTube comments following the overturn of Roe v. Wade, investigating how perceptions of health implications differ based on commenters’ gender and abortion stance. Methods: Using Netlytic, 25,730 comments were extracted from YouTube videos discussing the overturn of Roe v. Wade, half of which featured physicians discussing public health implications. Manual coding of 21% of the comments identified discussions on abortion stance and medical implications, while Gender API approximated the commenters’ gender. A term co-occurrence network was generated with VOSviewer to visualize key terms and their interrelations. Custom overlays explored patterns related to gender, abortion views, and medical implications, and comparisons within these overlays intersected with the medical implications overlay to illustrate contextual differences across demographics. Results: Four clusters emerged in the network: Constitutional Law, addressing the U.S. Constitution’s interpretation and legal impacts; Reproductive Rights and Responsibility, discussing alternatives to abortion and access; Human Development, exploring the intersection of abortion laws and individual beliefs; and Religious Beliefs, linking abortion laws to faith. Prochoice users focused on medical and socioeconomic impacts on women, whereas prolife users emphasized the prevention of unwanted pregnancies and moral considerations. Gender analysis revealed males centered on constitutional issues, while females highlighted medical and personal effects. Conclusion: The findings underscore that monitoring YouTube discourse offers valuable insights into public responses to shifts in health policy.

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Publication Details

Journal
Informatics
Published
2025-05-14
DOI
https://doi.org/10.3390/informatics12020049
Citations
2
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
4.96

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article

Analyzing the Overturn of Roe v. Wade: A Term Co-Occurrence Network Analysis of YouTube Comments

Elina Erzikova, Rodina Bizri-Baryak, Gary L. Kreps, Lana Ivanitskaya
2 citations
Informatics
Hate Speech and Cyberbullying Detection
4.96
article

Analyzing the Overturn of Roe v. Wade: A Term Co-Occurrence Network Analysis of YouTube Comments

Elina Erzikova, Rodina Bizri-Baryak, Gary L. Kreps, Lana Ivanitskaya
article en
2 citations

Abstract

Objective: This study examines YouTube comments following the overturn of Roe v. Wade, investigating how perceptions of health implications differ based on commenters’ gender and abortion stance. Methods: Using Netlytic, 25,730 comments were extracted from YouTube videos discussing the overturn of Roe v. Wade, half of which featured physicians discussing public health implications. Manual coding of 21% of the comments identified discussions on abortion stance and medical implications, while Gender API approximated the commenters’ gender. A term co-occurrence network was generated with VOSviewer to visualize key terms and their interrelations. Custom overlays explored patterns related to gender, abortion views, and medical implications, and comparisons within these overlays intersected with the medical implications overlay to illustrate contextual differences across demographics. Results: Four clusters emerged in the network: Constitutional Law, addressing the U.S. Constitution’s interpretation and legal impacts; Reproductive Rights and Responsibility, discussing alternatives to abortion and access; Human Development, exploring the intersection of abortion laws and individual beliefs; and Religious Beliefs, linking abortion laws to faith. Prochoice users focused on medical and socioeconomic impacts on women, whereas prolife users emphasized the prevention of unwanted pregnancies and moral considerations. Gender analysis revealed males centered on constitutional issues, while females highlighted medical and personal effects. Conclusion: The findings underscore that monitoring YouTube discourse offers valuable insights into public responses to shifts in health policy.

InformaticsVol. 12(2)
George Mason University (US), Central Michigan University (US), University of Michigan–Dearborn (US)
Central Michigan University
Openalex Percentile: Top 5%
Hate Speech and Cyberbullying Detection
4.96
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