Trends and Gaps in Public Perception of Genetic Testing for Dementia Risk: Unsupervised Deep Learning of Twitter Posts From 2010 to 2023.

Background: Genetic testing for dementia has drawn public attention in recent years, albeit with concerns on its appropriate use. This study leveraged Twitter data to analyze public perceptions related to genetic testing for dementia.

Methods: English tweets from January 1, 2010 to April 1, 2023, containing relevant terms, were extracted from Twitter API. A Bidirectional Encoder Representations from Transformers (BERT) model was used with Named Entity Recognition (NER) to identify individual and organizational users. BERT-based topic modeling was applied to identify the themes for relevant source tweets. Topic coherence was assessed through manual inspection, complemented by the Silhouette Coefficient. Manual thematic analysis, following Braun and Clarke's approach, refined the topics and themes.

Results: The analysis of 3045 original/source tweets identified 9 topics (Silhouette Coefficient=0.19), categorized into 3 main themes: (1) opinions on the appropriateness of genetic testing in dementia diagnosis; (2) discussion on the psychosocial impact; (3) discussion on genetic testing's role in Alzheimer's disease treatment and prevention. Theme 1 comprised 90.6% of source tweets, demonstrating prevailing contentions. Tweets in theme 2 were increasingly contributed by organization users over time and included tweets containing misinformation about genetic testing in children. Tweets in theme 3 were increasingly contributed by individual users, possibly suggesting rising public interest in the treatment and prevention of dementia.

Conclusion: The study highlighted limited public understanding of the nondeterministic nature of genetic testing for dementia, with concerns about unsupervised direct-to-consumer genetic test marketing, emphasizing the need to counter misinformation and raise public awareness.