Voices of People With Disabilities: Integrating Topic Modeling and Sentiment Analysis to Study Disability Discourse on Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People with Disability (PwD) are some of society’s marginalized and vulnerable groups. They are mostly disadvantaged because accessibility to communal structures and social services remains challenging. Sometimes, PwDs are misunderstood because not all disabilities are visible or outward, which makes it difficult to implement useful interventions for them. Thus, the voices of PwDs, as expressed freely on social media must be studied to understand better the fundamental challenges they face. In this research, we analyze the comments expressed in Disability communities on Reddit in the last 5 years (from 2019 to 2024) to uncover the concerns and sentiments of PwDs. Comments were collected through the Reddit API from 4 Disability subreddits, namely r/ADHD, r/Blind, r/deaf, and r/disability. Overall, a total of 601,215 comments were extracted for analysis. We applied topic modeling algorithms, namely Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and two variations of BERTopic (BERTopic with K-means clustering and BERTopic with HDBSCAN clustering) on each subreddit’s comments to extract hidden topics. The NMF discovered 15 topics in the r/Blind and 20 topics in the r/deaf. Furthermore, related topics were merged into themes, and we discovered 9 themes in both r/ADHD and r/Blind, 8 themes in r/deaf, and 7 themes in r/disability. Additionally, a pre-trained transformer, SiEBERT, was used to determine the sentiments for the themes in each subreddit. The themes discovered across at least 2 subreddits are Mobility, Diagnosis, Education, Assistive and Accessible Technology, Support, Disability Accommodations, and Relations. PwD with ADHD struggle with the effects of medications, household chores, sleep, attention span, and oversubscribing to online payment services. The PwD who are visually impaired feel alienated by society, struggle with public transit systems, have limited employment, and experience harassment. Those with difficulty hearing express difficulty with hearing devices, educational materials, technological challenges, limited workplace accommodations, and bad treatment from people. Our research discussed the themes and provided recommendations where applicable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it