Using Machine Learning to Track Disability Discourse on Social Media
Bibliographic record
Abstract
People with Disabilities (PWDs) have so much to contribute to society positively and progressively. However, they are mostly disadvantaged, discriminated against, and given limited opportunities. PWDs can experience different forms of physiological and psychological barriers. To complicate matters, PWDs are often misunderstood so without a proper all-inclusive stakeholder engagement, suitable solutions cannot be proffered. Appreciatively, social media platforms have offered forums for participatory discourse, and PWDs can express their views and opinions on these platforms. These views and sentiments can be analyzed using machine learning techniques to better understand the PWDs. Thus, it is the goal of this research to study the concerns of PWDs as expressed on Reddit over the last 5 years. In this work, we evaluate the performance of three topic models in the extraction of topics from the collected Reddit comments. The models are the Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and BERTopic. We leveraged the BERTopic with the K-means clustering algorithm, which achieved a coherence score of 0.67 to discover 15 meaningful and coherent topics that were then narrowed into 7 major themes. The themes discovered include disability benefits, mobility, medical conditions, support, assistive technology, COVID-19 effects, and emotional support animals. The SiEBERT model was employed to analyze the comments to identify the positive and negative sentiments associated with each theme.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".