Stories of Control and Decline: Analyzing the effects of overturning Roe v. Wade on US politicians’ narratives

This study explores the effects of the U.S. Supreme Court's decision to overturn Roe v. Wade on the framing of abortion narratives by House and Senate representatives. The landmark decision, which occurred on June 24, 2022, marked a significant shift in abortion policy. This research is motivated by the need to understand the political ramifications of this decision and how it influences political communication strategies. The key question I address in this study is whether and how the narratives about abortion by House and Senate representatives changed following the overturning of Roe v. Wade. The study focuses on identifying differences in story types and the overall sentiment of narratives before and after the decision. I hypothesize that Republican representatives adjusted their story types to appeal to voters in the midterm election. To analyze the narratives, I use an advanced text-as-data approach, including structural topic modeling, supervised text classification and sentiment analysis, and the Narrative Policy Framework (NPF). The dataset consists of 5,293 tweets from 405 House and Senate representatives discussing abortion in 2022. The NPF is used to identify four specific story types outlined by Stone (2012) and to understand how representatives with varying policy agendas employ these narratives. Preliminary results reveal an increase in stories of decline narrated by Republican representatives following the overturn indicating a shift in their ideological stance possibly to attract back votes. The sentiment of Republicans’ narratives is more negative even when they are not stories of decline. The implications of this research are twofold. First, it underscores the dynamic nature of political narratives and their adaptability in response to major policy changes, contributing to our understanding of how political actors adjust their communication strategies to resonate with constituents and the broader public. Second, it advances the Narrative Policy Framework by incorporating computational text analysis, enabling faster and more efficient automatic classification of story types compared to manual coding.