Location inference for hidden population with online text analysis
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International Journal of Health Geographics Open Access
RESEARCH
Location inference for hidden population with online text analysis Chuchu Liu1*†, Ziqiang Cao1*† and Xin Lu1,2*
Abstract 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 geostatistics for other hidden populations. Keywords: Location inference, Hidden population, MSM, Text analysis, Geographic distribution Introduction A population is “hidden” when no sampling frame exists and public acknowledgment of membership in the population is potentially threatening [1–3]. *Correspondence: [email protected]; [email protected] † Chuchu Liu and Ziqiang Cao contributed equally to this work 1 College of Systems Engineering, National University of Defense Technology, Changsha 410073, China Full list of author information is available at the end of the article
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