ArticleBMC medical informatics and decision making2018
Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Analytic Methods to Investigate HIV-Related Digital Communication: A Scoping Review.Current HIV/AIDS reports · 2025Article
- Decoding HIV Discourse on Social Media: Large-Scale Analysis of 191,972 Tweets Using Machine Learning, Topic Modeling, and Temporal Analysis.Journal of medical Internet research · 2025Article
- Harnessing Big Heterogeneous Data to Evaluate the Potential Impact of HIV Responses Among Key Populations in Sub-Saharan Africa: Protocol for the Boloka Data Repository Initiative.JMIR research protocols · 2025Article
- What can we learn from a Chinese social media used by glaucoma patients?BMC ophthalmology · 2023Article
- A Study on Online Health Community Users' Information Demands Based on the BERT-LDA Model.Healthcare (Basel, Switzerland) · 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
- Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing.JMIR formative research · 2022Article
- Implementation of respondent driven sampling in Nairobi, Kenya, for tracking key family planning indicators among adolescents and youth: lessons learnt.BMC research notes · 2022Article
- Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes.International journal of environmental research and public health · 2022Article
- Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes.Journal of medical Internet research · 2021Article
- Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis.Healthcare (Basel, Switzerland) · 2021Article
- Getting resources to those who need them: the evidence we need to budget for underserved populations in sub-Saharan Africa.Journal of the International AIDS Society · 2021Article
- Location inference for hidden population with online text analysis.International journal of health geographics · 2020Article
- Determining the Topic Evolution and Sentiment Polarity for Albinism in a Chinese Online Health Community: Machine Learning and Social Network Analysis.JMIR medical informatics · 2020Article
- Study on Differences between Patients with Physiological and Psychological Diseases in Online Health Communities: Topic Analysis and Sentiment Analysis.International journal of environmental research and public health · 2020Article
- Depressive Emotion Detection and Behavior Analysis of Men Who Have Sex With MenFrontiers in psychiatry · 2020Article
- Network Evolution of a Large Online MSM Dating Community: 2005-2018.International journal of environmental research and public health · 2019Article
- Inferring Opinions and Behavioral Characteristics of Gay Men with Large Scale Multilingual Text from Blued.International journal of environmental research and public health · 2019Article
- Inferring the Population Mean with Second-Order Information in Online Social Networks.Entropy (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundTraditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community.
methodsBy elaborately designing crawlers, we retrieved a complete dataset from the "HIV bar," the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure.
resultsIn HIV communities, the average topic similarity among members is positively correlated to network efficiency (r = 0.70, p < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size (r = 0.25, p = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests.
conclusionsOnline communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.
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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.