Analyzing the Overturning of Roe vs Wade on Twitter using Natural Language Processing Techniques
Gabriela Pinto · 2023
In 1973, the historic U.S. Supreme Court (SCOTUS) case of Roe vs. Wade provided the constitutional rightto abortion. However, on May 2, 2022, Politico magazine leaked the draft opinion on the Dobbs v. Jackson Women’s Health Organization. The leak generated a surge of users to post their opinion on the case that would eliminate abortion as a constitutional right. Then, on June 24, 2022, SCOTUS overturned Roe vs. Wade. In this thesis, we aim to investigate the public opinion and reaction towards the overturning of Roe vs. Wade. We collected 20,640,166 tweets using Twitter API for Academic Research and an open-sourced dataset published during two periods. The first period was a week before Politico magazine leaked theSCOTUS decision and the week after. The second period was a week before and over a week after theoverturning of Roe vs. Wade. Using natural language processing techniques, including sentiment analysis,emotion recognition, topic modeling, and bi-grams, we could develop insight into public opinion based onthe posted tweets. Our research investigates if there is a change in sentiment over time, a change in theemotion expressed within the text over time, and which topics are most common within the collection oftweets. The results demonstrate a significant increase on the day of the Politico leak, which showed thatmost of the tweets published on that day expressed a positive sentiment. However, in the weeks before andafter the overturning of Roe v. Wade, we witness a decrease from the beginning of the period up to the dayof the overturn. Regarding emotion recognition, there is a significant decrease in the proportion of tweetsclassified as expressing optimism. There’s also an increase in the proportion of tweets expressing anger when comparing the day of the Politico leak and the day of the overturn. The topic model we applied to thetweets published on the day of the Politico leak revealed that states’ rights and children were discussed. Using bigram of the most negative tweets, we witnessed gun control and healthcare as words that frequently occurred within the collection.