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.

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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

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article

Utilizing Large Language Models to Monitor Social Media for Disability: An Analysis of Sentiment and Disability Models in Tweets

Christo El Morr, Vijay Mago, Hannah Bullock, Abdul Hamid Dabboussi et al.
Studies in health technology and informatics
Text Readability and Simplification
article

Utilizing Large Language Models to Monitor Social Media for Disability: An Analysis of Sentiment and Disability Models in Tweets

Christo El Morr, Vijay Mago, Hannah Bullock, Abdul Hamid Dabboussi, N.E. Jacob, Iman Yousuf
article en

Abstract

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.

Studies in health technology and informaticsVol. 327
York University (CA)
York University
Quality Education
Openalex Percentile: Top 81%
Text Readability and Simplification
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