Empirical analysis of online human dynamics

Zhi-Dan Zhao and Tao Zhou

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 11, 3308-3315

Abstract: Patterns of human activities have attracted increasing academic interests, since the quantitative understanding of human behavior is helpful to uncover the origins of many socioeconomic phenomena. This paper focuses on behaviors of Internet users. Six large-scale systems are studied in our experiments, including the movie-watching in Netflix and MovieLens, the transaction in Ebay, the bookmark-collecting in Delicious, and the posting in FreindFeed and Twitter. Empirical analysis reveals some common statistical features of online human behavior: (1) The total number of user’s actions, the user’s activity, and the interevent time all follow heavy-tailed distributions. (2) There exists a strongly positive correlation between user’s activity and the total number of user’s actions, and a significantly negative correlation between the user’s activity and the width of the interevent time distribution. We further study the rescaling method and show that this method could to some extent eliminate the different statistics among users caused by the different activities, yet the effectiveness depends on the data sets.

Keywords: Human dynamics; Power-law distribution; Activity; Interevent time distribution (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:11:p:3308-3315

DOI: 10.1016/j.physa.2012.01.008

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