Examining ethno-racial attitudes of the public in Twitter discourses related to the United States Supreme Court ruling: A machine learning approach.

BACKGROUND

The decision of the US Supreme Court to repeal 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 .

METHODS

We screened approximately 3 million tweets posted to 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% (= 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% (= 12,092) expressed negative sentiments, while 39.0% (= 81,45) expressed positive sentiments, and 3.0% (= 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 and supports the need for active surveillance of racial and ethnic disparities in abortion access in the post- era.