Utilizing Large Language Models to Monitor Social Media for Disability: An Analysis of Sentiment and Disability Models in Tweets
This study explores how well large language models (like the kind that powers ChatGPT) can analyze online conversations about disability rights. We specifically looked at whether these models could: 1) identify if tweets about people with disabilities were positive or negative, and 2) tell if the tweets viewed disability as a problem with society (social model) or a problem with the individual (medical model). We collected 5,000 tweets and trained a language model to analyze them. The results demonstrated promising accuracy levels for sentiment analysis and social vs. medical model classification.
Authors
- Christo El Morr (ORCID: https://orcid.org/0000-0001-6287-3438)
- Vijay Mago (ORCID: https://orcid.org/0000-0002-9741-3463)
- Hannah Bullock (ORCID: https://orcid.org/0000-0003-3377-3045)
- Abdul Hamid Dabboussi (ORCID: https://orcid.org/0000-0002-8376-9637)
- N.E. Jacob
- Iman Yousuf
Institutions
- York University (CA)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250612
- Primary Topic
- Text Readability and Simplification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- York University