# HIV Prevention and Care

> **NIH NIH R00** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $248,752

## Abstract

Project Summary
I propose to implement an innovative mixed methods study using machine learning techniques for text
analytics and predictive modeling in combination with advanced social network analysis to provide valuable
information about the online communicative and social contexts that contribute to HIV prevention and care
engagement among young Black men who have sex with men (YBMSM). The study aims to answer the
following questions: (1) How are the semantic features of social media communication (i.e., key terms and
concepts) among YBMSM related to their HIV prevention and care engagement?; (2) How are structural
features of observed social media relationships among YBMSM related to their prevention and care
engagement?; and (3) Can social media use patterns predict future prevention and care engagement? To
pursue these questions, I will draw on data collected March 2016-March 2019 from YBMSM participants in an
ongoing network HIV prevention intervention (N=423). Prevention and care engagement behaviors include
retention in care (HIV/PrEP/Primary), STI/HIV testing, and condom use. Participant social media data include:
(1) Facebook posts, (2) Facebook friendships, and (3) Facebook group memberships. K99 Phase: Research
conducted during the K99 Phase will use machine learning techniques for textual analysis, semantic network
characterization, and regression models to identify the semantic features of participants' communication and
their association with HIV prevention and care engagement. I will receive mentored training in 5 key areas: (1)
knowledge of issues relevant to HIV prevention, care and treatment for MSM; (2) machine learning techniques
for text analytics and predictive modeling; (3) advanced stochastic network modeling, including exponential
random graph models (ERGMs); and (4) design and implementation of social media based interventions for
health promotion; and (5) professional development. R00 Phase: For the R00 I will employ one-mode and two-
mode ERGMs to determine how prevention and care engagement behaviors affect the structure of observed
social media relationships. I will then develop a predictive model for prevention and care engagement on the
basis of individuals' social media use patterns. This K99/R00 award mechanism will be critical to my success in
achieving long-term success as an expert in the social and communicative dynamics of HIV prevention and
care engagement in high-risk populations. It will also pave the way to an R01 application – based on a
longitudinal design – of YMSMs' social and communication engagement in a more diverse array of online
social networking sites, including both general purpose and dating platforms. The goal of this project will be to
utilize that information to establish classes of YMSM on the basis of their heterogeneous patterns of social
media use to better understand their synergistic effects on HIV prevention and care engagement practices over
time. This is an important next step...

## Key facts

- **NIH application ID:** 10253483
- **Project number:** 4R00HD094648-03
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Lindsay Erin Young
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $248,752
- **Award type:** 4N
- **Project period:** 2018-08-31 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10253483

## Citation

> US National Institutes of Health, RePORTER application 10253483, HIV Prevention and Care (4R00HD094648-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10253483. Licensed CC0.

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