# Mining Social Media Messages for HIV Testing and Prevention Communication

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $514,311

## Abstract

PROJECT SUMMARY
The spread of the SARS-CoV-2 in the US has created novel problems with methods successfully underway in
the parent grant that use cutting edge, Big Data techniques for processing vast and diverse data (text, videos,
images) to select actionable and acceptable social media HIV messages that match the HIV biomedical needs
of target US counties. These problems include: (a) the messages we are currently selecting are not relevant in
the context of the pandemic and the current machine learning methods take many months to optimize; (b)
combination SARS-CoV-2/HIV messages would be necessary but the methods to produce them have not been
developed; and (c) no methods to multiply scarce or new messages have been validated. This emergency-
supplement application requests the resources to offset these problems and to use the opportunity to generate
new knowledge about HIV and responses to pandemics. Our new approach will use the same successfully
tested techniques to select, generate, and deliver combination messages about HIV (i.e., testing, PrEP, and
condom use) and messages about social distancing, testing, prevention, and treatment of the SARS-CoV-2,
specifically for Men who have Sex with Men (MSM). The current pandemic also serves as a reminder that
vaccination is lacking in MSM; therefore, the supplemental project will integrate messaging about vaccines,
including an eventual one against SARS-CoV-2. To counteract the new problems resulting from insufficient
numbers of appropriate HIV messages for the SARS-CoV-2 times and to adapt current methods which are too
time-consuming and inflexible for messaging in such a changing environment, we need (1) new, rapid methods
to identify actionable and acceptable messages; (2) new methods to combine HIV recommendations with
emerging public health recommendations related to SARS-CoV-2; and (3) new methods to rapidly multiply the
resulting messages for social media. Thus, this funding application will pursue the following: Aim 1. Extend
current methods to identify regional needs related to the SARS-CoV-2 pandemic and vaccines, Aim 2.
Deploy new rapid methods of message selection for a pandemic, and Aim 3. Experimentally test the
effect of the new methods by sending (a) the selected experimental messages (combined with links to
local service information) to managers of social media accounts and (b) a random selection of
HIV/SARS-CoV-2 messages to a group of control counties. This significant and innovative project will be
facilitated by unique team expertise in communication and persuasion, Big Data methods, public health, and
Bayesian spatio-temporal modeling, and by the participation of leading institutions in the areas of psychology,
public health, and computer science. The project is synergistic with new data collection efforts associated with
the American Men Internet Survey, which will now collect SARS-CoV-2 seroprevalence data, which will be
supplemented to conduct additional behavioral data fo...

## Key facts

- **NIH application ID:** 10480894
- **Project number:** 5R01MH114847-05
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** DOLORES ALBARRACIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $514,311
- **Award type:** 5
- **Project period:** 2018-09-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10480894

## Citation

> US National Institutes of Health, RePORTER application 10480894, Mining Social Media Messages for HIV Testing and Prevention Communication (5R01MH114847-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10480894. Licensed CC0.

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