With the advent of Big Data methods, social media is the proverbial low-hanging fruit to disseminate HIV prevention and testing messages on a large scale 1,2. These media already transmit messages on condom use, HIV testing, and Pre-Exposure Prophylaxis (PrEP), from government institutions, NGOs, private citizens, and community groups, but it does so in an informal way. This real-time repository of real-world health messages, along with our ability to mine and pinpoint counties that need to engage in a conversation about HIV prevention and testing, offers a unique opportunity to develop Big Data methods for geographically targeted message dissemination. Despite some interventions designed for online delivery 3?19, the overall potential of social media and their most promising contents (e.g., actionable messages with behavioral instructions) have surprisingly not been established to date. Our project will focus on the disease-burdened population of Men Who Have Sex With Men (MSM), and will develop a highly significant computing infrastructure (Aim 1) to automatically and continuously input social media postings from Twitter, Facebook, and Instagram, behavioral data from the American Men Internet Survey (AMIS), and HIV prevalence data from AIDSVu.org, and using that triangulation, to target counties that need social media messages about condom use, HIV testing, and/or PrEP for MSM. Using machine learning methods, the same platform will then select actionable and acceptable messages to fill county gaps. Once the platform has been refined with input from research participants who are employees of health departments, it will be used to send experimental messages (Aim 2), selected to match county needs, and to be actionable and acceptable to a group of health departments randomized to the experimental condition. The success of the experimental messages will be gauged by the hallmarks of social media, repostings, Likes, Dislike, and comment favorability, compared with the success of a random selection of HIV-relevant messages sent to a different group of health departments randomized to the control condition. The project is innovative in several ways. First, the social media messages will involve diverse inputs (text, images, videos) never before brought together in this area. Second, we are not aware of the prior use of the proposed triangulation involving epidemiological, behavioral, and social media data. Third, a method of mining naturally accruing messages will be new and transformative, allowing for the generation of ?live? campaigns with messages selected that remain current, sustainable, and community-based by design. Further, the use of an implementation-science experiment at a large, geographically distributed, scale is highly novel.
These research aims are facilitated by unique team expertise about communication and persuasion, Big Data methods, public health, and Bayesian spatio-temporal modeling, and leading institutions in the areas of psychology, public health, and computer science.
This project is concerned with identifying a method to disseminate public health information through social media in an optimal way. Findings from this project will inform prevention efforts in this area as well as in fighting other diseases in which social influences are important.
