JMIR Publications

ABSTRACT

Interventions are sorely needed to address the lack of PrEP awareness and mitigate barriers related to PrEP use. One such intervention modality is social media, as PrEP awareness and communicating issues, such as access and cost, are easily addressable via clear social media messages on platforms PrEP-eligible people, and especially young people, use frequently. This study seeks to extend understanding of PrEP awareness and usage by examining PrEP-related communication across 3 popular social media platforms (Facebook, Instagram, and Twitter), and identifying message and source characteristics. In February 2023, we used CrowdTangle (a public-insights tool owned by Facebook, now known as Meta) to gather a total of 39,790 Facebook posts and 5,628 Instagram posts. We also used Twitter’s public API to collect 14,061 Twitter posts during the same time frame. Of these, we drew a random sample of social media posts from each platform [Facebook (N = 1,000), Instagram (N = 1,000), and Twitter (N = 811)] in February 2023 and analyzed them using a quantitative content analysis. Our findings showed some differences in the type of text-based content most likely to appear on each platform. We also uncovered similar patterns across all 3 platforms. Across all platforms, we observed that definitions of and indications for PrEP were the most common type of text-based content in posts likely to be shared, information about PrEP appearing in social media posts did not seem to draw from traditional sources, and men who have sex with men (MSM) represented the most frequently mentioned target population. Although our study did not detect a large presence of theory-based concepts from behavior change theory such as the reasoned action approach (RAA), across all platforms, attitude emerged most frequently, followed by self-efficacy. These findings shed light on the PrEP-related beliefs shaping young people’s perceptions and engagement. Such insights can guide the design of future social media–based messages, targeting the most influential beliefs to strengthen HIV prevention efforts. They also provide a foundation for advanced machine learning models capable of predicting and explaining the diffusion potential of PrEP-related content.