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Where Are WeChat Users

A Geolocation Method Based on User Missequence State Analysis

Bibliographic Data

ID22108173
AuthorsWenqi Shi (0000-0001-8972-7342, PLA Information Engineering University), Xiangyang Luo (0000-0001-6062-2950, PLA Information Engineering University), Jiadong Guo (0000-0002-1498-2194, PLA Information Engineering University), Chong Liu (0009-0007-7425-8847, PLA Information Engineering University), Fenlin Liu (0000-0001-8019-1713, PLA Information Engineering University)
Year2021
Volume8
Issue2
Pages319-331
Publication date2021-04-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2021.3049120
OpenAlexW3122074088
LanguageEN
References cited18

WeChat has earned more than one billion users worldwide. Research on the geolocation of WeChat users can not only discover the location of malicious users but also verify the validity of user privacy protection strategies. However, existing methods are susceptible to WeChat's location confusion strategies, resulting in intolerable geolocating errors. In this article, a WeChat user geolocation method based on user missequence state analysis (MSAG) is proposed. Different from the existing methods which usually rely on the relationship between reported and actual distances of nearby users, MSAG utilizes the relation between user order and actual distances to geolocate the target. By statistical analysis of sequence changes of nearby users under different actual distances, the distance range that causes nearby users missequence is determined. During geolocation, the distance range of the target is delimited by checking the missequence state of the target and a user with a known location. Finally, we discuss trilateration strategies for different abnormal situations. Experimental results show that MASG can achieve high-precision geolocation of WeChat users, the average error is less than 50 m, and 72% of geolocating errors are within 60 m; compared with existing typical algorithms, the average error is reduced by 23.7%-50.7

Confusion · Data mining · Geolocation · Real-time computing · Trilateration · World Wide Web · Caching and Content Delivery · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Opportunistic and Delay-Tolerant Networks

Highly citedNo

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