# Mining Social Media Messages for HIV Testing and Prevention Communication

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2020 · $546,504

## Abstract

PROJECT SUMMARY
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 ...

## Key facts

- **NIH application ID:** 9988534
- **Project number:** 5R01MH114847-03
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** DOLORES ALBARRACIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $546,504
- **Award type:** 5
- **Project period:** 2018-09-15 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9988534

## Citation

> US National Institutes of Health, RePORTER application 9988534, Mining Social Media Messages for HIV Testing and Prevention Communication (5R01MH114847-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9988534. Licensed CC0.

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