Psoriasis is a chronic skin disorder, and patients encounter high physical and psychosocial burdens. Social media forums feature extensive patient-generated comments. We hypothesized that analyzing patient-posted comments using natural language processing would provide insights into patient engagements, sentiments, concerns, and support, which are vital for the holistic management of psoriasis. We collected 32,000 active user comments posted on Reddit. We applied Latent Dirichlet Allocation to categorize posts into popular topics and employed spectral clustering to establish cohesive themes and word representation frequency within these topics. We sorted posts into 29 significant topics of discussion and categorized them into four categories: management (37.48%), emotion (21.57%), presentation (19.79%), and others (3.57%). The frequent posts on management were diet (7.23%), biologics (6.95%), and adverse effects (3.88%). The emotion category comprised negative sentiments (11.02%), encouragement (5.49%), and gratitude (5.06%). The presentation topic included a discussion of scalp (5.69%), flare-timing (3.63%), and arthritis (2.64%). Others comprised differential diagnosis (5.01%), leaky gut (4.12%), and referrals (3.70%). This study identified patients' experiences and perspectives associated with psoriasis, which should be considered to tailor support systems to improve their quality of life.
Keywords: dermatology; machine learning; patient engagement; psoriasis; social media.
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