Understanding Administrative Burdens and Experiences of Social Program Beneficiaries and Applicants Using Posts on Reddit

• In Event: Understanding SNAP’s Reach and Impact: Evidence from Households, School Programs, and Markets

Abstract

Previous research has documented the extent to which applicants and beneficiaries of social programs often face administrative burdens, which can result in negative experiences and reduced participation rates. While prior work has explored personal experiences with administrative processes through surveys and interviews conducted at single points in time, such methods may not fully capture the real-time, evolving, and candid experiences of individuals. This study examines the barriers and challenges associated with social programs, particularly the Supplemental Nutrition Assistance Program (SNAP), by analyzing posts from Reddit, a large online discussion forum. 

We analyzed posts on the “r/foodstamps” subreddit (a Reddit community focused on SNAP) between 2015 and March 2024. During this period, there were approximately 11,500 new original posts and about 100,000 comments (including replies from original posters) on these posts. Using these posts, we identified common discussion topics and associated sentiments using natural language processing. Our preliminary results indicate that frequently discussed topics include“EBT card,” “caseworker and interview,” and“eligibility of family members.” Among the most common adjectives used were “worried” and “confused,” which appeared most frequently in discussions about caseworkers and interviews. We also found that the words “apply,” “phone,” and “letter” were most commonly associated with posts expressing negative sentiments, suggesting their negative experience with the application process. Moreover, we examined how the distribution of topics and sentiments changed over time.

Through the application of topic modeling and sentiment analysis, our study provides policy-relevant insights into the administrative burdens experienced by beneficiaries and applicants, using organically generated and real-time data from online platforms.

Authors

• Michiko Ueda, Syracuse University
  Presenting Author

• Colleen M Heflin, Syracuse University
  Non-Presenting Co-Author