Home
Issues
Aim and Scope
Open Access
Editorial Board
Indexing
Why publish with us
Contact Us
Instructions to Authors (PDF)
Manuscript Types
Manuscript Formatting
How to submit
Preprints
Special publications & Reprints
Authorship & COI
Principles of Transparency Checklist
Data Policies
Publication Ethics and Publication Malpractice Statement
The use of artificial intelligence and natural language processing for visualizing social media discussions surrounding the repeal of Roe v. Wade in the United States of America
1
Central Michigan University, 122 Falling Rock Way, United States
2
Central Michigan University, 1200 S. Franklin St., Mount Pleasant, Mich. 48859, United States
Publication date: 2023-04-26
Popul. Med. 2023;5(Supplement):A1860
ABSTRACT
Background and Objective:
We present a novel method of network visualization with computer-assisted processing of text. We leverage human and computer collaboration to develop and validate algorithms. We apply a network analysis and natural language processing to the corpus of 28,693 YouTube comments surrounding the repeal of Roe v. Wade. In addition, we connect visualizations to qualitative study, to deepen our exploration of social media users’ understanding of policy and implications for women’s health.
Methods:
We analyzed comments to nine most watched YouTube newscasts about the repeal of Roe v. Wade by ABC, CBS, CNN, Fox News, MSNC and Vice News. We applied a Gender API through google and computer-scored social and psychological states from Linguistic Inquiry Word Count (LIWC-22). VOSviewer visualizations of Roe v. Wade relevant terms extracted from YouTube comments are overlayed with computer-generated LIWC scores.
Results:
Our colorful map – a network of 256 interconnected terms extracted from 28,693 comments has 4 thematic clusters of terms: 1) overturning of Roe v. Wade/states’ rights/political debate/Supreme Court rulings/abortion rights; 2) Consequences of eliminating the right to choose/possible solutions; and 3) Argument for determining when life begins/fetal development/health complications; 4) Religion and morality. Clusters 2 and 3 score high on female usernames (Gender API), in-text references to females (LIWC), and health related concerns. In contrast, male usernames are more prevalent in Clusters 1 and 4 with discussions on religion, policy, and the political implications of the Supreme Court decision.
Conclusion:
Eye-catching term networks and LIWC overlays help researchers visualize and communicate about policy issues, as they are deliberated in social media. Overlayed with gender-specific information, networks of co-occurring terms can assist in audience segmentation. To make data analysis more efficient, some human validation and curation is needed with automated text scoring to identify discussion areas with specific linguistic features.
We present a novel method of network visualization with computer-assisted processing of text. We leverage human and computer collaboration to develop and validate algorithms. We apply a network analysis and natural language processing to the corpus of 28,693 YouTube comments surrounding the repeal of Roe v. Wade. In addition, we connect visualizations to qualitative study, to deepen our exploration of social media users’ understanding of policy and implications for women’s health.
Methods:
We analyzed comments to nine most watched YouTube newscasts about the repeal of Roe v. Wade by ABC, CBS, CNN, Fox News, MSNC and Vice News. We applied a Gender API through google and computer-scored social and psychological states from Linguistic Inquiry Word Count (LIWC-22). VOSviewer visualizations of Roe v. Wade relevant terms extracted from YouTube comments are overlayed with computer-generated LIWC scores.
Results:
Our colorful map – a network of 256 interconnected terms extracted from 28,693 comments has 4 thematic clusters of terms: 1) overturning of Roe v. Wade/states’ rights/political debate/Supreme Court rulings/abortion rights; 2) Consequences of eliminating the right to choose/possible solutions; and 3) Argument for determining when life begins/fetal development/health complications; 4) Religion and morality. Clusters 2 and 3 score high on female usernames (Gender API), in-text references to females (LIWC), and health related concerns. In contrast, male usernames are more prevalent in Clusters 1 and 4 with discussions on religion, policy, and the political implications of the Supreme Court decision.
Conclusion:
Eye-catching term networks and LIWC overlays help researchers visualize and communicate about policy issues, as they are deliberated in social media. Overlayed with gender-specific information, networks of co-occurring terms can assist in audience segmentation. To make data analysis more efficient, some human validation and curation is needed with automated text scoring to identify discussion areas with specific linguistic features.
| ISSN: | 2654-1459 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
