Heterogeneous characters modeling of instant message services users’ online behavior
Name
journal.pone.0195518.pdf
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10.6 MB
Format
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Checksum (MD5)
a960f99c7cd0eb5c98997cc3242f3327
Author(s)
Cui, Hongyan
Li, Ruibing
Horn, Berthold
Fang, Yajun
Horn, Berthold K. P.
Welsch, Roy E
Date Issued
May 2018
Journal
PLOS ONE
Publisher
Public Library of Science (PLoS)
Citation
Cui, Hongyan, Ruibing Li, Yajun Fang, Berthold Horn, and Roy E. Welsch. “Heterogeneous Characters Modeling of Instant Message Services Users’ Online Behavior.” Edited by Xiao-Pu Han. PLOS ONE 13, no. 5 (May 7, 2018): e0195518. © 2018 Cui et al.
Version
Final published version
Abstract
Research on temporal characteristics of human dynamics has attracted much attentions for its contribution to various areas such as communication, medical treatment, finance, etc. Existing studies show that the time intervals between two consecutive events present different non-Poisson characteristics, such as power-law, Pareto, bimodal distribution of power-law, exponential distribution, piecewise power-law, et al. With the occurrences of new services, new types of distributions may arise. In this paper, we study the distributions of the time intervals between two consecutive visits to QQ and WeChat service, the top two popular instant messaging services in China, and present a new finding that when the value of statistical unit T is set to 0.001s, the inter-event time distribution follows a piecewise distribution of exponential and power-law, indicating the heterogeneous character of IM services users’ online behavior in different time scales. We infer that the heterogeneous character is related to the communication mechanism of IM and the habits of users. Then we develop a combination model of exponential model and interest model to characterize the heterogeneity. Furthermore, we find that the exponent of the inter-event time distribution of the same service is different in two cities, which is correlated with the popularity of the services. Our research is useful for the application of information diffusion, prediction of economic development of cities, and so on.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PONE.0195518
