ArticlePloS one2020
Use of social media big data as a novel HIV surveillance tool in South Africa.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of infodemiology studies using artificial intelligence: social media posts on HIV preexposure prophylaxis.AIDS (London, England) · 2025Pooled it
- What social media analyses can tell us about Ghanaian women's concerns during pregnancy.Frontiers in digital health · 2025Article
- Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.Current HIV/AIDS reports · 2024Review
- Utilization of Social Media for the Prevention and Control of HIV/AIDS: A Scoping Review.Journal of multidisciplinary healthcare · 2024Article
- Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.Current HIV/AIDS reports · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- Using Twitter Data Analysis to Understand the Perceptions, Awareness, and Barriers to the Wide Use of Pre-Exposure Prophylaxis in the United States.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- The role of social media in monitoring COVID-19 vaccine uptake.Journal of evaluation in clinical practice · 2022Article
- Reliability of COVID-19 data: An evaluation and reflection.PloS one · 2022Article
- Technology-Delivered Intervention Strategies to Bolster HIV Testing.Current HIV/AIDS reports · 2021Review
- Power of Big Data in ending HIV.AIDS (London, England) · 2021Article
- Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19.Frontiers in public health · 2021Article
- From data to decisions: The vital role of digital epidemiology for strengthening public health.Digital healthReview
- Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Sub-Saharan Africa has been heavily impacted by the HIV/AIDS epidemic. Social data (e.g., social media, internet search, wearable device, etc) show great promise assisting in public health and HIV surveillance. However, research on this topic has primarily focused in higher resource settings, such as the United States. It is especially important to study the prevalence and potential use of these data sources and tools in low- and middle-income countries (LMIC), such as Sub-Saharan Africa, which have been heavily impacted by the HIV epidemic, to determine the feasibility of using these technologies as surveillance and intervention tools. Accordingly, we 1) described the prevalence and characteristics of various social technologies within South Africa, 2) using Twitter, Instagram, and YouTube as a case study, analyzed the prevalence and patterns of social media use related to HIV risk in South Africa, and 3) mapped and statistically tested differences in HIV-related social media posts within regions of South Africa. Geocoded data were collected over a three-week period in 2018 (654,373 tweets, 90,410 Instagram posts and 14,133 YouTube videos with 1,121 comments). Of all tweets, 4,524 (0.7%) were found to related to HIV and AIDS. The percentage was similar for Instagram 95 (0.7%) but significantly lower for YouTube 18 (0.1%). We found regional differences in prevalence and use of social media related to HIV. We discuss the implication of data from these technologies in surveillance and interventions within South Africa and other LMICs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.