Immediate Study about Themes Emerged in the Twitter Posts about the Roe v. Wade Leaked

The present study instantaneously aims at analyzing the themes that have cropped up in the Twitter posts about the Roe v. Wade leaked version and to explore the themes which have cropped up in sampled Twitter data of the leaked version. Data, in this present study, is based on a sample of Twitter tweets on the leaked Roe v.Wade ruling. It identifies themes and emotions within textual data by text mining, sentiment analysis, and topic modeling. These are then incorporated into the MDCOR system, thus making available a fully fledged toolset for large-scale textual data analysis. Results: The findings extract two main theme codes and their sentiment distribution, along with their high-frequency words. The results indicated that the most used keywords were "roevswade," "abortionrights," and "woman." Sentiment analysis shows negative, fear, and sadness as the dominant feelings. Therefore, this study provides a data foundation for investigating the themes emerging in Twitter posts regarding the Roe v. Wade leaked version, able to help enterprises or governments develop intervention strategies and understand public opinion.

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Publication Details

Journal
Communications in Humanities Research
Published
2025-02-21
DOI
https://doi.org/10.54254/2753-7064/2024.21072
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Immediate Study about Themes Emerged in the Twitter Posts about the Roe v. Wade Leaked

Xiaoyan Zheng, Jiawei Luo, Yifan Duan, Xinyang Xiong et al.
Communications in Humanities Research
Hate Speech and Cyberbullying Detection
article

Immediate Study about Themes Emerged in the Twitter Posts about the Roe v. Wade Leaked

Xiaoyan Zheng, Jiawei Luo, Yifan Duan, Xinyang Xiong, Kaiki Xiong
article en

Abstract

The present study instantaneously aims at analyzing the themes that have cropped up in the Twitter posts about the Roe v. Wade leaked version and to explore the themes which have cropped up in sampled Twitter data of the leaked version. Data, in this present study, is based on a sample of Twitter tweets on the leaked Roe v.Wade ruling. It identifies themes and emotions within textual data by text mining, sentiment analysis, and topic modeling. These are then incorporated into the MDCOR system, thus making available a fully fledged toolset for large-scale textual data analysis. Results: The findings extract two main theme codes and their sentiment distribution, along with their high-frequency words. The results indicated that the most used keywords were "roevswade," "abortionrights," and "woman." Sentiment analysis shows negative, fear, and sadness as the dominant feelings. Therefore, this study provides a data foundation for investigating the themes emerging in Twitter posts regarding the Roe v. Wade leaked version, able to help enterprises or governments develop intervention strategies and understand public opinion.

Communications in Humanities ResearchVol. 56(1)
University of Liverpool (GB), University of California System (US), BASIS International (United States) (US), Fuzhou University (CN)
Openalex Percentile: Top 99%
Hate Speech and Cyberbullying Detection
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Immediate Study about Themes Emerged in the Twitter Posts about the Roe v. Wade Leaked — Xiaoyan Zheng, Jiawei Luo, et al. · Communications in Humanities Research (2025) | TGRS Research Map | TGRS