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Modeling correlated human dynamics with temporal preference

Author

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  • Wang, Peng
  • Zhou, Tao
  • Han, Xiao-Pu
  • Wang, Bing-Hong

Abstract

We empirically study the activity pattern of individual blog-posting and observe the interevent time distributions decay as power-laws at both individual and population level. As different from previous studies, we find significant short-term memory in it. Moreover, the memory coefficient first decays in a power law and then turns to an exponential form. Our findings produce evidence for the strong short-term memory in human dynamics and challenge previous models. Accordingly, we propose a simple model based on temporal preference, which can well reproduce both the heavy-tailed nature and the strong memory effects. This work helps in understanding the temporal regularities of online human behaviors.

Suggested Citation

  • Wang, Peng & Zhou, Tao & Han, Xiao-Pu & Wang, Bing-Hong, 2014. "Modeling correlated human dynamics with temporal preference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 145-151.
    • Handle: RePEc:eee:phsmap:v:398:y:2014:i:c:p:145-151
    DOI: 10.1016/j.physa.2013.12.014
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    Citations

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    Cited by:

    1. Sun, Zhi & Peng, Qinke & Lv, Jia & Zhong, Tao, 2017. "Analyzing the posting behaviors in news forums with incremental inter-event time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 203-212.
    2. Wang, Wenjun & Yuan, Ning & Pan, Lin & Jiao, Pengfei & Dai, Weidi & Xue, Guixiang & Liu, Dong, 2015. "Temporal patterns of emergency calls of a metropolitan city in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 846-855.
    3. Rashidisabet, Homa & Ajilore, Olusola & Leow, Alex & Demos, Alexander P., 2022. "Revisiting power-law estimation with applications to real-world human typing dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).

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