Using Machine Learning to Track Disability Discourse on Social Media
Sandra Kumi, Charles C. Snow, Joseph Marfo-Gyimah, Richard K. Lomotey, Ralph Deters · 2024
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.