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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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How do online social networks grow?

Konglin Zhu1, Wenzhong Li2, Xiaoming Fu1

  • 1Institute of Computer Science, Georg-August-Universität Göttingen, Göttingen, Germany.

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Summary

Online social network growth shows scaling patterns, but long-range temporal correlations are not significant. Independent growth processes with varied entry rates explain observed behaviors in platforms like Gowalla.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Statistical Physics

Background:

  • Online social networks (OSNs) like Facebook and Twitter are crucial for studying individual behavior, group dynamics, and societal emergence.
  • Understanding the growth patterns and underlying processes of OSNs is essential for network science and computational social science.

Purpose of the Study:

  • To characterize the average growth of online social networks.
  • To investigate the processes driving seemingly long-range temporal correlated collective behavior in OSNs.
  • To contrast growth patterns in different OSNs with established economic growth laws.

Main Methods:

  • Analysis of average growth rates and standard deviations in OSNs.
  • Comparison with Gibrat's law of proportionate growth.
  • Scaling analysis to detect temporally long-range correlated behavior.
  • Decomposition of growth processes into independent components.

Main Results:

  • Scaling observed in average growth rate and standard deviation, contrasting with Gibrat's law.
  • Renren and Twitter exhibit significant deviations from typical social and economic systems.
  • While Gowalla shows potential long-range temporal correlations, these are explained by independent growth processes with diverse entry rates.

Conclusions:

  • Seemingly long-range temporal correlations in OSN growth, like Gowalla's, can be attributed to independent growth processes with varied entry rates.
  • Temporally or spatially correlated behavior does not appear to be a major factor in the growth of most online social networks.