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Human activity in the web.

Filippo Radicchi1

  • 1Complex Networks Lagrange Laboratory (CNLL), Institute from Scientific Interchagne (ISI), 10133 Torino, Italy. f.radicchi@gmail.com

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Summary
This summary is machine-generated.

Analyzing web activity data reveals that while global interevent times are not representative of individual user behavior, normalized interevent times show universal patterns. Waiting times, however, accurately reflect individual user activity.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Human-Computer Interaction

Background:

  • The digital age generates vast datasets on human behavior, primarily from web interactions.
  • Understanding individual online activity patterns is crucial for various applications.

Purpose of the Study:

  • To analyze interevent and waiting-time statistics in large-scale web activity datasets.
  • To determine the representativeness of global distributions for individual user behavior.
  • To identify universal patterns in human online activities.

Main Methods:

  • Analysis of three large datasets detailing human web activities.
  • Detailed study of interevent time and waiting-time statistics.
  • Application of nonparametric statistical tests to assess distribution reliability.

Main Results:

  • Both interevent and waiting times exhibit power-law decay as the time difference increases.
  • Global interevent time distributions are poor predictors of single-user behavior due to heterogeneity in operation counts.
  • Normalized interevent times reveal universal behavior across users.
  • Waiting-time distributions are user-activity independent and accurately represent individual replying patterns.

Conclusions:

  • Individual web activity patterns are complex and not fully captured by global distributions.
  • Normalization of interevent times is key to uncovering universal behavioral dynamics.
  • Waiting-time analysis offers a reliable method for understanding individual user engagement.