Designing for Discourse: Social Media, Socio-Technical Rhetorical Strategies, and Affirmative Action Discussions
Authors
Social media platforms enable diverse users to engage in everyday political talk with (un)known audiences. Platform features and affordances may shape political discussions and how audiences make sense of them, potentially shifting political attitudes. Using affirmative action (AA) – a controversial, identity-centric higher education policy – as a context for analysis, we investigate social media features’ and affordances’ role in AA discussions. Our qualitative content analysis of over 38,000 social media posts and comments across Reddit, Twitter/X, and TikTok demonstrates how features (e.g., Green Screen) and affordances (e.g., visibility) shape the presentation of external evidence and cues on social media that help users determine information veracity. We introduce socio-technical rhetorical strategies to describe rhetorical devices enabled by platform features and affordances and consider how these strategies are used to express and refute racism online. Finally, we suggest ways that social media designers may leverage visibility, navigability, and association affordances to enhance users’ ability to make sense of and safely experience AA discussions.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 71%
Opening research commissioning to civic participation: creating a community panel to review the social impact of HCI research proposals.
CHI '21· Empowerment of Marginalized Groups +2
- 67%
Empowerment in HCI - A Survey and Framework
CHI '18· Empowerment of Marginalized Groups +1
- 63%
"If we post, what will people think of us?”: Offline Norms, Online Engagement and Unpacking Gendered Experiences in a Pakistani Facebook Tech Community
CHI '26· Social Platform Design & User Behavior +3
- 63%
Counter-Visual Artifacts: Negotiating Surveillance and Carceral Visuality in Public Housing through Videovoice
CHI '26· Technology Ethics & Critical HCI +3
Based on Jaccard similarity of research subtopics & professions (≥60%)