Spatiotemporal Analysis of Men Who Have Sex With Men in Mainland China: Social App Capture-Recapture Method

Hu, Maogui; Xu, Chengdong; Wang, Jinfeng

doi:10.2196/14800

Cited by 34 publications

(27 citation statements)

References 32 publications

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“…And the top 5 provinces with most relevant users are Sichuan, Guangdong, Zhejiang, Jiangsu and Hunan. These results are consistent with statistics of Chinese MSM population in recent studies [ 50 , 51 ], indicating that MSM are mainly distributed in large cities in the eastern and southwestern China which is more economically developed and culturally open. Compared with the location distribution extracted from user profiles, MSM distribution inferred by the algorithm is more accurate.…”

Section: Resultssupporting

confidence: 92%

Location inference for hidden population with online text analysis

Liu

Cao

2020

Int J Health Geogr

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Background Understanding the geographic distribution of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequate deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level. Methods We collect a comprehensive dataset from the largest sub-community related to MSM topics in Baidu Tieba, covering 628,360 MSM-related users. Based on users’ publicly available posts, we evaluate and compare the performances of mainstream location inference algorithms on the online locating problem of Chinese MSM population. To improve the inference accuracy, other approaches in natural language processing are introduced into the location extraction, such as context analysis and pattern recognition. In addition, we develop a hybrid voting algorithm (HVA-LI) by allowing different approaches to vote to determine the best inference results, which guarantees a more effective way on location inference for hidden population. Results By comparing the performances of popular inference algorithms, we find that the classic gazetteer-based algorithm has achieved better results. And in the HVA-LI algorithms, the hybrid algorithm consisting of the simple gazetteer-based method and named entity recognition (NER) is proven to be the best to deal with inferring users’ locations disclosed in short texts on online communities, improving the inferring accuracy from 50.3 to 71.3% on the MSM-related dataset. Conclusions In this study, we have explored the possibility of location inferring by analyzing textual content posted by online users. A more effective hybrid algorithm, i.e., the Gazetteer & NER algorithm is proposed, which is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.

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Section: Resultssupporting

confidence: 92%

Location inference for hidden population with online text analysis

Liu

Cao

2020

Int J Health Geogr

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show abstract

“…And the top 5 provinces with most relevant users are Sichuan, Guangdong, Zhejiang, Jiangsu and Hunan. These results are consistent with statistics of Chinese MSM population in recent studies (37,38), indicating that MSM are mainly distributed in large cities in the eastern and southwestern China which is more economically developed and culturally open. Compared with the location distribution extracted from user profiles, MSM distribution inferred by the algorithm is more accurate.…”

Section: Msm Distribution Inferred By the Gazetteer And Ner Algorithmsupporting

confidence: 91%

Location Inference for Hidden Population with Online Text Analysis

Liu

Cao

2020

Preprint

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Understanding the demographics of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequately deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level. The performances of popular inference algorithms are compared to elucidate the most suitable approach. In addition, we propose a new hybrid model, which is proven to achieve the highest accuracy for inferring locations of online users only based on text content. This method is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.

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“…As the political, economic and cultural center of China, Beijing has a population of 21.54 million people, among which 7.65 million are internal immigrants [34]. Beijing was purposively selected for it has a large influx of MSM every year [35][36][37][38].…”

Section: Study Sitementioning

confidence: 99%

HIV incidence among men who have sex with men using geosocial networking smartphone application in Beijing, China: an open cohort study

Zhang

Ren

et al. 2021

Infect Dis Poverty

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Background Sexual transmission among men who have sex with men (MSM) is the dominant route of HIV transmission in China. Extensive use of geosocial networking (GSN) smartphone application (app) has dramatically changed the pattern of sexual behaviors and HIV risk among MSM, but data on HIV incidence and the changing risk behaviors of GSN app-using MSM are limited. We aims to assess the HIV incidence and its correlates among gay GSN app-using MSM in China. Methods We constructed an open cohort which was initiated and maintained using a GSN app to assess the HIV incidence among app-using MSM, recruited from June 2017 to December 2018. MSM completed an online questionnaire on their sociodemographic characteristics, sexual behaviors, recreational drug use and sexually transmitted infections status. Then each man had an HIV test, and those tested negatives were enrolled into the cohort. Participants completed follow-ups with additional HIV tests though the app during the study period, and were censored at HIV seroconversion or study end date. HIV incidence was calculated by dividing the sum of observed HIV seroconversions by the observed person-time. Univariate (Chi-square test and Fisher’s exact test) and multivariate (proportional hazards regression) analyses were used to examine correlates of HIV incidence. Results A total of 6957 HIV negative MSM were enrolled in the open cohort, 37 seroconversions occurred among 1937 men contributing 1065 observed person-years: HIV incidence was 3.47 per 100 person-years [95% confidence interval (CI): 2.37–4.57]. More than five sexual partners [hazard ratio (HR) = 2.65, 95% CI: 1.04–6.67], and sex with HIV positive partners (HR = 3.82, 95% CI: 1.16–12.64) in the preceding six months were positively associated with HIV seroconversion. Consistent condom use for anal sex (HR = 0.27, 95% CI: 0.07–0.96), and reporting insertive anal sex only (HR = 0.23, 95% CI: 0.08–0.62) in the preceding six months were protective factors for HIV seroconversion. Conclusions Tailored interventions targeting app-using MSM are urgently needed given their high risk of HIV. As a new tool for accessing MSM at higher HIV risk, GSN smartphone app could play an important role in HIV research among MSM.

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Spatiotemporal Analysis of Men Who Have Sex With Men in Mainland China: Social App Capture-Recapture Method

Cited by 34 publications

References 32 publications

Location inference for hidden population with online text analysis

Location inference for hidden population with online text analysis

Location Inference for Hidden Population with Online Text Analysis

HIV incidence among men who have sex with men using geosocial networking smartphone application in Beijing, China: an open cohort study

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