Understanding Public Discourse on Alzheimer’s Disease and Dementia: A Sentiment Analysis and Topic Modeling Study of Social Media Data

Background: Alzheimer’s disease (AD) and dementia are growing global health challenges, yet public understanding of these conditions remains poorly characterized. Methods: This study analyzed 17, 578 English-language tweets from Twitter/X collected throughout 2024 to characterize public discourse using dual sentiment analysis and topic modeling. Sentiment was assessed using two lexicon-based tools, VADER (Valence Aware Dictionary and sEntiment Reasoner) and TextBlob, with inter-method agreement evaluated via Cohen’s Kappa and confusion matrix analysis. Latent Dirichlet Allocation (LDA) with coherence-based model selection (cᵥ) identified latent discussion topics. Results: All reported sentiment proportions are tool-specific estimates rather than absolute characterisations of public discourse and should be interpreted in light of the fair inter-method agreement observed between VADER and TextBlob. VADER classified 43. 3% of tweets as positive, 32. 2% as negative, and 24. 5% as neutral (mean compound score = 0. 065, SD = 0. 499; 95% CI: 0. 058 to 0. 073; note that the large SD relative to the mean reflects the broad, near-zero-centred distribution of compound scores). TextBlob produced a partially divergent distribution (45. 9% positive, 36. 2% neutral, 17. 8% negative) with fair inter-method agreement (κ = 0. 297), indicating that exact sentiment proportions depend meaningfully on the choice of classifier. Approximately 14% of tweets contained political content related to the U. S. presidential election cycle, which may represent an important source of noise in health discourse. LDA identified six optimal topics (cᵥ = 0. 407): research studies, blood-based diagnostics, comorbidities and personal loss, research advocacy, clinical developments, and risk factors and treatment. Sentiment differed significantly across topics (Kruskal–Wallis H = 235. 92, p < 0. 001), with research and advocacy topics showing the most positive VADER-classified sentiment (50. 0%) and comorbidity discussions showing the highest negative proportion (41. 4%). Positive tweets received significantly higher engagement than negative tweets (Mann–Whitney p < 0. 001), though follower count was the strongest predictor of engagement (β = 0. 198). Conclusions: These findings, interpreted as tool-conditional estimates, have implications for health communication strategies, public education campaigns, stigma reduction, and the responsible design of social media-based health surveillance systems.