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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: May 6, 2025
Open Peer Review Period: May 7, 2025 - Jul 2, 2025
Date Accepted: Jul 7, 2025
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

Zhan X, Song M, Shrader CH, Forbes C, Algarin A

Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

J Med Internet Res 2025;27:e76745

DOI: 10.2196/76745

PMID: 40882217

PMCID: 12396797

Decoding HIV Discourse on Social Media: A Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

  • Xiangming Zhan; 
  • Meijia Song; 
  • Cho Hee Shrader; 
  • Chad Forbes; 
  • Angel Algarin

ABSTRACT

Background:

HIV remains a global challenge, with stigma, financial constraints, and psychosocial barriers preventing people living with HIV from accessing healthcare services, driving them to seek information and support on social media. Despite the growing role of digital platforms in health communication, existing research often narrowly focuses on specific HIV-related topics rather than offering a broader landscape of thematic patterns. Additionally, much of the existing research lacks large-scale analysis, and predominantly predates COVID-19 and the platform’s transition to X, limiting our understanding of the comprehensive, dynamic, and post-pandemic HIV-related discourse.

Objective:

This study aimed to: (1) observe the dominant themes in current HIV-related social media discourse; (2) explore similarities and differences between theory-driven(e.g., literature-informed predetermined categories) and data-driven themes(e.g., unsupervised LDA without prior categorization); and (3) examine how emotional responses and temporal patterns influence the dissemination of HIV-related content.

Methods:

We analyzed 191,972 tweets collected between June 2023 and August 2024 using an integrated analytical framework. This approach combined: (1) supervised machine learning for text classification, (2) comparative topic modeling with both theory-driven and data-driven Latent Dirichlet Allocation (LDA) to identify thematic patterns, (3) sentiment analysis using VADER and NRC Emotion Lexicon to examine emotional dimensions, and (4) temporal trend analysis to track engagement patterns.

Results:

Theory-driven themes revealed that Information & Education content constituted the majority of HIV-related discourse (63.02%), followed by Opinions & Commentary (12.43%) and Personal Experiences & Stories (10.25%). The data-driven approach identified eight distinct themes, some of which shared similarities with aspects from the theory-driven approach while others were unique. Temporal analysis revealed two different engagement patterns: official awareness campaigns like World AIDS Day generated delayed peak engagement through top-down information sharing, while community-driven events like National HIV Testing Day showed immediate user engagement through peer-to-peer interactions.

Conclusions:

HIV-related social media discourse on X reflects the dominance of informational content, the emergence of prevention as a distinct thematic focus, and the varying effectiveness of different timing patterns in HIV-related messaging. These findings suggest that effective HIV communication strategies can integrate medical information with community perspectives, maintain balanced content focus, and strategically time messages to maximize engagement. These insights provide valuable guidance for developing digital outreach strategies that better connect healthcare services with vulnerable populations in the post-COVID-19 pandemic era.


 Citation

Please cite as:

Zhan X, Song M, Shrader CH, Forbes C, Algarin A

Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis

J Med Internet Res 2025;27:e76745

DOI: 10.2196/76745

PMID: 40882217

PMCID: 12396797

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