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Exploring Stance on Affirmative Action Through Reddit Narratives
Conference proceeding   Open access

Exploring Stance on Affirmative Action Through Reddit Narratives

Aria Pessianzadeh and Rezvaneh Rezapour
Proceedings of the 17th ACM Web Science Conference 2025, pp 52-63
20 May 2025
DOI: https://doi.org/10.1145/3717867.3717891
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.1145/3717867.3717891View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0,  Open

Abstract

Applied computing Computing methodologies -- Natural language processing
Affirmative Action (AA), is a controversial topic that aims to address historical inequalities in education and employment by considering race, gender, and ethnicity during the selection process. While some view AA as a necessary tool for promoting diversity and correcting systemic discrimination, others criticize it as reverse discrimination, arguing it unfairly favors certain groups. These conflicting views have led to heated debates, legal battles, and polarized public discourse. This study explores narratives of AA on social media, focusing on how people express their positions on this issue using stance analysis. After collecting 3,839 posts from 50 subreddits, we developed fine-grained stance categories to capture the nuances of this controversial discourse on Reddit and used LLM-based classifiers to identify stances in our data. Our results suggest that the majority of users on Reddit oppose AA in its current format, while many express skepticism or raise questions about it. Additionally, our topic modeling results highlight a broad range of themes related to societal, cultural, legal, and political aspects of AA. Finally, moral analysis indicates the prevalence of Fairness and Authority in AA narratives. Our work contributes to a better understanding of public attitudes toward AA and provides insights into people’s perspectives on social media. We also contribute to stance analysis methodologies, highlighting the complexities involved in detecting diverse opinions on highly charged topics. Warning: This paper includes language and content that may be offensive or triggering.

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Web of Science research areas
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
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