Big Data Research ›› 2018, Vol. 4 ›› Issue (5): 15-28.doi: 10.11959/j.issn.2096-0271.2018047

• TOPIC:PRACTICAL INNOVATIONS OF BIG DATA • Previous Articles     Next Articles

Analysis of online activity characteristics of hidden populations based on public data

Chuchu LIU1,Xin LU1,2,3()   

  1. 1 College of Systems Engineering,National University of Defense Technology,Changsha 410073,China
    2 School of Business,Central South University,Changsha 410083,China
    3 Department of Public Health Sciences,Karolinska Institute,Stockholm 17177,Sweden
  • Online:2018-09-15 Published:2018-12-10
  • Supported by:
    The National Natural Science Foundation of China(71522014);The National Natural Science Foundation of China(71771213);The National Natural Science Foundation of China(71790615);The National Natural Science Foundation of China(71431006)

Abstract:

Data from 36 Baidu Tieba was collected.The sub-groups of users from three dimensions of time (temporal),content (text) and interaction (network) were analyzed,and patterns of their online activity were tried to extract and characteristics of different populations was inferred.The result indicates that the temporal pattern of posting behavior is more regular for HIV-related users.Communities followed by these users are also HIV-related,revealing a significant clustering pattern.On the other hand,MSM groups are more active during midnight,and their motivation of online activities in the community is for entertainment and for meeting partners.There is also a strong preference of following the same type of Tiebas for MSM groups.In general,HIV-related users are very concerned about their health status.On the contrary,MSM-related users are lack of awareness for protection and prevention of AIDS.

Key words: hidden population, AIDS, MSM, online features, complex network

CLC Number: 

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