"Analyzing patterns of user content generation in online social networks"

Lei Guo, Enhua Tan, Songqing Chen, Xiaodong Zhang, and Yihon (Eric) Zhao

Proceedings of 15th ACM SIGKDD Conference on Knowledge Discovery and Data 
Mining, (KDD-09), Paris, France, June 28 - July 1st, 2009. 


Abstract

Various online social networks (OSNs) have been developed
rapidly on the Internet. Researchers have analyzed different
properties of such OSNs, mainly focusing on the formation
and evolution of the networks as well as the information
propagation over the networks. In knowledge-sharing
OSNs, such as blogs and question answering systems, issues
on how users participate in the network and how users 
"generate/contribute" knowledge are vital to the sustained and
healthy growth of the networks. However, related discussions
have not been reported in the research literature.
In this work, we empirically study workloads from three
popular knowledge-sharing OSNs, including a blog system,
a social bookmark sharing network, and a question answering  
social network to examine these properties. Our analysis
consistently shows that (1) users. posting behavior in these
networks exhibits strong daily and weekly patterns, but the
user active time in these OSNs does not follow exponential
distributions; (2) the user posting behavior in these OSNs 
follows stretched exponential distributions instead of power-law
distributions, indicating the influence of a small number of
core users cannot dominate the network; (3) the distributions
of user contributions on high-quality and effort-consuming
contents in these OSNs have smaller stretch factors for the
stretched exponential distribution. Our study provides insights 
into user activity patterns and lays out an analytical foundation  
for further understanding various properties of these OSNs.