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Joachim Mathiesen1, Luiza Angheluta, Peter T H Ahlgren

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Human activity on social media and financial markets shows bursts and quiet periods, driven by real-world events. This correlated behavior follows predictable patterns, consistent with a stochastic point process model.

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Area of Science:

  • Complex systems science
  • Quantitative finance
  • Computational social science

Background:

  • Massive social organizations exhibit complex, correlated human activity.
  • Real-world events often trigger bursts in social media engagement and financial trading volumes.
  • Empirical data reveals intermittent fluctuations with long-range temporal correlations.

Purpose of the Study:

  • To analyze correlated human activity in large social systems using empirical data.
  • To model the observed intermittent fluctuations and temporal correlations.
  • To validate a stochastic point process model against real-world observations.

Main Methods:

  • Utilized empirical data from Twitter and financial securities trading volumes.
  • Analyzed the occurrence rate of international brand names and trading volumes.
  • Described human activity using a stochastic point process and derived its distribution from a stochastic differential equation.

Main Results:

  • Observed intermittent fluctuations in human activity, characterized by bursts and quiescent periods.
  • Identified an inverse cubic power-law tail in the distribution of activity levels.
  • Demonstrated long-range temporal correlations with a characteristic power spectrum.
  • Found the derived distribution and power spectrum to be consistent with empirical data.

Conclusions:

  • The study successfully models correlated human activity in massive social organizations.
  • A stochastic point process accurately captures the observed intermittent dynamics and temporal correlations.
  • Findings provide insights into the collective behavior of large social systems.