LAUSR

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.