The Voices We Don’t Hear: Identifying Barriers to SNAP Participation from Reddit Discussions

• In Event: Access and Retention in SNAP and WIC: State-Level Patterns and Interventions

Abstract

Research Objective

A central challenge in public administration and social policy research is understanding why many eligible individuals do not apply for public benefits. Administrative data offers little insight into non-participants, and even high-quality surveys struggle to capture the nuanced emotional and informational barriers that shape disengagement. This study proposes a novel approach: using social media data from Reddit as a theory-induction tool to identify and characterize individuals who are left out of the benefit system. The primary objective is to uncover who does not apply for the Supplemental Nutrition Assistance Program (SNAP), why they don’t, and how their experiences vary across demographic and geographic contexts. I focus on the subreddit r/foodstamps—a public forum where users seek help, share frustrations, and describe their interactions with SNAP. While not representative of the full SNAP population, this digital community surfaces rich narratives from individuals navigating the edges of eligibility and access.

Theory

This study builds on the theory of administrative burden (Herd & Moynihan, 2018), which conceptualizes how learning, compliance, and psychological costs reduce access to public programs. However, most burden research focuses on those already enrolled. I extend this framework by turning attention to non-participants and marginally attached users—individuals who consider applying but ultimately do not. In doing so, I recenter benefit access research on disengagement as a meaningful and policy-relevant outcome in itself, rather than treating it as a byproduct of noncompliance or individual choice. I also incorporate an additional category of burden that appears frequently in user narratives: eligibility and policy-related confusion, including uncertainty around qualifications, documentation, and shifting rules.

Method

I analyzed posts from the subreddit r/foodstamps spanning 2015 to 2022, which capture public discourse around SNAP across varying policy environments. To extract structure from these unstructured narratives, I used large language models (LLMs) guided by custom-designed chain-of-thought prompts, allowing the model to reason through textual cues to infer each user’s intent, demographic characteristics, and perceived barriers. I assessed the reliability of this approach using a series of checks: consistency across repeated runs, agreement across model types, and sensitivity to prompt variation. This method enables a granular view of public engagement with food assistance programs—including those who never applied, those who considered applying but were deterred, and those who actively navigated or exited the system.

Implications

This study introduces Reddit discussions as a novel data source for public management, offering unsolicited, real-time insight into how individuals experience and interpret benefit programs. These narratives illuminate the lived realities of administrative burden and reveal patterns of disengagement not captured in official data. Methodologically, this work demonstrates the potential of combining LLM-based inference with public digital discourse to extend the reach of benefit access research. Substantively, it brings non-participants into focus—inviting scholars and policymakers to view disengagement not as silence, but as a meaningful form of policy feedback.

Author

• Zhaowen Guo, Georgetown University
  Presenting Author