โก Quick Summary
This study conducted a large-scale analysis of 191,972 tweets related to HIV discourse on social media, utilizing machine learning and topic modeling to uncover dominant themes and emotional responses. The findings highlight the prevalence of informational content and the importance of timing in HIV messaging strategies.
๐ Key Details
- ๐ Dataset: 191,972 tweets collected from June 2023 to August 2024
- ๐งฉ Analytical Methods: Supervised machine learning, topic modeling (LDA), sentiment analysis, and temporal trend analysis
- ๐ Focus Period: Post-COVID-19 era
๐ Key Takeaways
- ๐ Dominant Themes: 63.02% of tweets focused on information and education about HIV.
- ๐ฌ Emotional Engagement: Different engagement patterns were observed based on the type of content shared.
- ๐ Community vs. Official Messaging: Community-driven events generated immediate engagement, while official campaigns saw delayed peaks.
- ๐ง Data-Driven Insights: Eight distinct themes emerged from the data-driven analysis, some overlapping with theory-driven themes.
- ๐ Implications for Strategy: Effective HIV communication should blend medical information with community perspectives.
- โฐ Timing Matters: Strategic timing of messages can enhance engagement and outreach effectiveness.
๐ Background
HIV continues to pose a significant global health challenge, with various barriers such as stigma and financial constraints hindering access to healthcare services. As individuals increasingly turn to social media for information and support, understanding the broader landscape of HIV-related discourse becomes essential. This study aims to fill the gap in existing research by providing a comprehensive analysis of HIV discussions on social media platforms, particularly in the context of the ongoing pandemic.
๐๏ธ Study
The research team analyzed a substantial dataset of tweets to identify the dominant themes in HIV-related discourse. By employing a combination of supervised machine learning and topic modeling, the study aimed to explore both theory-driven and data-driven thematic patterns. Additionally, sentiment analysis was conducted to assess emotional responses, while temporal analysis tracked engagement trends over time.
๐ Results
The analysis revealed that the majority of tweets (63.02%) were centered around information and education, followed by opinions (12.43%) and personal experiences (10.25%). The data-driven approach identified eight unique themes, some of which aligned with the theory-driven findings. Notably, the study found that official awareness campaigns generated delayed engagement, contrasting with the immediate interactions seen during community-driven events.
๐ Impact and Implications
The insights gained from this study are crucial for developing effective HIV communication strategies. By recognizing the dominance of informational content and the varying effectiveness of different engagement patterns, healthcare providers can better connect with vulnerable populations. This research underscores the importance of integrating medical information with community perspectives and timing messages strategically to maximize outreach efforts in the post-COVID-19 landscape.
๐ฎ Conclusion
This study highlights the significant role of social media in shaping HIV discourse and the potential for leveraging these platforms to enhance health communication. By understanding the thematic patterns and emotional responses associated with HIV-related content, we can develop more effective outreach strategies that resonate with diverse audiences. The future of HIV communication looks promising, and continued research in this area is essential for improving health outcomes.
๐ฌ Your comments
What are your thoughts on the role of social media in HIV discourse? We invite you to share your insights and engage in a conversation! ๐ฌ Leave your comments below or connect with us on social media:
Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis.
Abstract
BACKGROUND: HIV remains a global challenge, with stigma, financial constraints, and psychosocial barriers preventing people living with HIV from accessing health care 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. In addition, much of the existing research lacks large-scale analysis and predominantly predates COVID-19 and the platform’s transition to X (formerly known as Twitter), limiting our understanding of the comprehensive, dynamic, and postpandemic HIV-related discourse.
OBJECTIVE: This study aims to (1) observe the dominant themes in current HIV-related social media discourse, (2) explore similarities and differences between theory-driven (eg, literature-informed predetermined categories) and data-driven themes (eg, unsupervised Latent Dirichlet Allocation [LDA] without previous 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 LDA to identify thematic patterns, (3) sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) and the NRC Emotion Lexicon to examine emotional dimensions, and (4) temporal trend analysis to track engagement patterns.
RESULTS: Theory-driven themes revealed that information and education content constituted the majority of HIV-related discourse (120,985/191,972, 63.02%), followed by opinions and commentary (23,863/191,972, 12.43%), and personal experiences and stories (19,672/191,972, 10.25%). The data-driven approach identified 8 distinct themes, some of which shared similarities with aspects from the theory-driven approach, while others were unique. Temporal analysis revealed 2 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.
Author: [‘Zhan X’, ‘Song M’, ‘Shrader CH’, ‘Forbes CE’, ‘Algarin AB’]
Journal: J Med Internet Res
Citation: Zhan X, et al. Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis. Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis. 2025; 27:e76745. doi: 10.2196/76745