Users' location analysis based on Chinese mobile social media. (5th September 2018)

• Record Type: Journal Article
• Authors: Wang, Zhibo; Guo, Yuechuan; Zheng, Senzhe; Xu, Wei; Liu, Lin; Liu, Zixin; Cui, Xiaohui
• Other Names: Hodon Michal guestEditor.; Furtak Janusz guestEditor.; Fahrnberger Güenter guestEditor.; Awad Ali I. guestEditor.; Cui Xiaohui guestEditor.; Suman Bilial guestEditor.; Wang Zhibo guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 13(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-05
• Subjects: big data -- location mining -- mobile social media -- web crawler; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4669
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: After the rapid development for more than 20 years, Internet has gradually become the main carrier of people's information and behaviors in people's daily life. In addition, the innovation and popularization of smartphone GPS makes user location information much more available and accurate, helping it to create remarkable values by which people are attracted to focus on social media–related data mining and applications. However, because of the sparsity of social media geographical information, direct inferences of locations have plenty of difficulties. Under the background of big data, this research has revised the UGC‐LI model in the preprocess of texts and the creation of the local dictionaries in which we take existed local dictionaries from the Internet into consideration, with the purpose of the inferences for users' and texts' locations. At the time of writing, through the crawler, we acquire users' personal information, the blog content, and customer relationships' (follows, fans) information more than 410 331 pieces from Sina Weibo. The experimental results show that the recall rate of the user location inference is 86.0%, whereas the precise rate is 77.4%, and the accuracy of text posted location inference is 66.8%. Compared with some other related algorithms, this revised model has comparatively better results in location inference for users and text publication.