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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Location inference for hidden population with online text analysis.

Chuchu Liu1, Ziqiang Cao2, Xin Lu3,4

  • 1College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China. liuchuchu15@nudt.edu.cn.

International Journal of Health Geographics
|December 10, 2020
PubMed
Summary
This summary is machine-generated.

Mapping hidden populations like men who have sex with men (MSM) is crucial for public health. This study uses online data and a hybrid algorithm to accurately infer user locations, improving mapping for these hard-to-reach groups.

Keywords:
Geographic distributionHidden populationLocation inferenceMSMText analysis

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Area of Science:

  • Computational Social Science
  • Geographic Information Systems (GIS)
  • Public Health Informatics

Background:

  • Geographic distribution of hidden populations (e.g., men who have sex with men, MSM) is vital for public health interventions.
  • Traditional survey methods face limitations due to accessibility and sensitivity issues with hidden populations.
  • Online communities offer a potential data source for mapping these groups.

Purpose of the Study:

  • To develop and evaluate location inferring methods for high-resolution mapping of MSM online communities in China.
  • To address the challenges of studying hard-to-access populations using publicly available online data.
  • To improve the accuracy of geographic inference for hidden populations.

Main Methods:

  • Collected a dataset of 628,360 users from a large MSM community on Baidu Tieba.
  • Evaluated and compared mainstream location inference algorithms using user posts.
  • Introduced natural language processing techniques like context analysis and pattern recognition.
  • Developed a hybrid voting algorithm (HVA-LI) for improved location inference accuracy.

Main Results:

  • The classic gazetteer-based algorithm performed well in location inference.
  • A hybrid algorithm combining gazetteer-based methods and Named Entity Recognition (NER) proved most effective.
  • The proposed hybrid algorithm improved inference accuracy from 50.3% to 71.3% for MSM users.

Conclusions:

  • Location inference from online user text is feasible and effective.
  • The proposed Gazetteer & NER algorithm overcomes sparse location labeling issues.
  • This approach can be extended for geo-statistical inference in other hidden populations.