Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.
Qing XuMatthew C NaliTiana J McMannHector GodinezJiawei LiYifan HeMingxiang CaiChristine LeeChristine MerendaRichardae AraojoTimothy K MackeyPublished in: JMIR infodemiology (2022)
Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.
Keyphrases
- social media
- men who have sex with men
- hiv testing
- hiv positive
- machine learning
- health promotion
- health information
- antiretroviral therapy
- human immunodeficiency virus
- hiv infected
- transcription factor
- hepatitis c virus
- artificial intelligence
- hiv aids
- big data
- current status
- mental health
- sleep quality
- south africa
- risk assessment
- mesenchymal stem cells
- african american
- bone marrow
- physical activity
- climate change