Voices of People With Disabilities: Integrating Topic Modeling and Sentiment Analysis to Study Disability Discourse on Social Media
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".