Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community.
Background: Traditional 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...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 18; pp. 1 - 11 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
1/5/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127173605&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127173605 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 1/5/2018 vid: 18 pid: 24147 pub: BioMed Central artinfo: ui: 127173605 127173605 NLM29304788 127173605 10.1186/s12911-017-0579-1 NLM29304788 127173605 ppf: 1 ppct: 10 formats: tig: atl: Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community. aug: au: Liu, Chuchu Lu, Xin affil: College of Information System and Management, National University of Defense Technology, 410073, Changsha, China sug: subj: Social Media Data Collection HIV Infections Psychosocial Factors Social Networking Models, Statistical Medical Informatics Human China Validation Studies Comparative Studies Evaluation Research Multicenter Studies Multidimensional Health Locus of Control Scales Social Readjustment Rating Scale ab: Background: Traditional 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.Methods: By 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.Results: In 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.Conclusions: Online 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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