Examination of the Public's Reaction on Twitter to the Over-Turning of Roe v Wade and Abortion Bans

Abortion pill marketing and sourcing on twitter following Dobbs v. Jackson supreme court ruling

OBJECTIVE

This study examines abortion-related discourse on Twitter (X) pre-and post-Dobbs v. Jackson ruling, which eliminated the constitutional right to abortion.

STUDY DESIGN

We used a custom data collection tool to collect tweets directly from Twitter using abortion-related keywords. We used the BERTopic language model and examined the top 30 retweeted and top 30 textually similar tweets from relevant topic clusters using an inductive coding approach. We also conducted statistical testing to assess potential associations between abortion themes.

RESULTS

166,799 unique tweets were collected from December 2020-December 2022. 464 unique tweets were coded for abortion-related themes with 154 identified as relevant. Of these, 66 tweets marketed abortion pills, 17 tweets were identified as offering consultations, and 91 tweets were relevant to self-managed abortion. All marketing and consultation tweets were posted post-Dobbs decision and 7 (7.69%) of self-managed tweets were posted pre-Dobbs versus 84 (92.30%) posted post-Dobbs. A positive association was found between tweets offering a medical consultation with tweets marketing abortion pills and discussing self-managed abortion.

CONCLUSION

This study detected online marketing of abortion pills, consultations and discussions about self-managed abortion following the Dobbs v. Jackson ruling. These results provide more context to the type of abortion-related information that is available online.

Public health and clinical implications of Dobbs v. Jackson for patients and healthcare providers: A scoping review

INTRODUCTION

On June 24, 2022, the U.S. Supreme Court's decision in Dobbs v. Jackson reversed the precedent set forth by Roe v. Wade, empowering individual states to regulate abortion care. This aftermath of this ruling has given rise to widespread bans, limiting the accessibility of abortion services for patients and impeding providers' ability to deliver a comprehensive spectrum of reproductive health services. Of particular concern is the disproportionate impact on medically underserved groups, further heightening existing social and structural disparities in reproductive health.

METHODS

We conducted a scoping review to broadly evaluate the clinical and public health impact of Dobbs on patients' access to abortion care and related reproductive health services, in addition to the training and clinical practice of healthcare providers. We searched eight bibliographic databases (PubMed, Scopus, Embase, PsycINFO, Google Scholar, Science Direct, JSTOR, and Web of Science) and three preprint servers (medRxiv, bioRxiv, and Europe PMC) using various combinations of keywords related to 'abortion', 'Dobbs', and 'Roe' on March 22, 2023. Four reviewers independently screened the studies based on pre-specified eligibility criteria and one reviewer performed data extraction for pre-identified themes. The search was conducted based on PRISMA Extension for Scoping Reviews (PRSIMA-ScR) guidelines.

RESULTS

Eighteen studies, comprising 12 peer-reviewed articles and 6 study abstracts, met the inclusion criteria. The studies demonstrated that Dobbs increased demand for contraception, magnified existing travel- and cost-related barriers to access, further polarized views on abortion and complex family planning on social media (e.g., Twitter), and evoked substantial concerns among medical trainees regarding their scope of practice and potential legal repercussions for providing abortion care.

CONCLUSION

In the wake of Dobbs v. Jackson, further public health and clinical interventions are urgently needed to bridge disparities in abortion care and reproductive health, mitigating the deleterious consequences of this emerging public health crisis.

Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.

Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis

BACKGROUND

The Supreme Court ruling in Dobbs v Jackson Women's Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information.

OBJECTIVE

This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information.

METHODS

We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest.

RESULTS

Our study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI -22.98% to -16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information-related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information-related terms and concomitant negative sentiment in the postdecision leak period.

CONCLUSIONS

The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information-related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.

Examining ethno-racial attitudes of the public in Twitter discourses related to the United States Supreme Court Dobbs vs. Jackson Women's Health Organization ruling: A machine learning approach

Background

The decision of the US Supreme Court to repeal Roe vs. Wade sparked significant media attention. Although primarily related to abortion, opinions are divided about how this decision would impact disparities, especially for Black, Indigenous, and people of color. We used advanced natural language processing (NLP) techniques to examine ethno-racial contents in Twitter discourses related to the overturn of Roe vs. Wade.

Methods

We screened approximately 3 million tweets posted to Roe vs. Wade discussions and identified unique tweets in English-language that had mentions related to race, ethnicity, and racism posted between June 24 and July 10, 2022. We performed lexicon-based sentiment analysis to identify sentiment polarity and the emotions expressed in the Twitter discourse and conducted structural topic modeling to identify and examine latent themes.

Results

Of the tweets retrieved, 0.7% (n = 23,044) had mentions related to race, ethnicity, and racism. The overall sentiment polarity was negative (mean = -0.41, SD = 1.48). Approximately 60.0% (n = 12,092) expressed negative sentiments, while 39.0% (n = 81,45) expressed positive sentiments, and 3.0% (n = 619) expressed neutral sentiments. There were 20 latent themes which emerged from the topic model. The predominant topics in the discourses were related to "racial resentment" (topic 2, 11.3%), "human rights" (topic 2, 7.9%), and "socioeconomic disadvantage" (topic 16, 7.4%).

Conclusions

Our study demonstrates wide ranging ethno-racial concerns following the reversal of Roe and supports the need for active surveillance of racial and ethnic disparities in abortion access in the post-Roe era.

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.