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Shaping Social Activity by Incentivizing Users.

Mehrdad Farajtabar1, Nan Du1, Manuel Gomez Rodriguez2

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Summary
This summary is machine-generated.

This study introduces a method to control online social network activity by adjusting external drives. Using Hawkes processes, it precisely steers network behavior towards desired states, outperforming other approaches.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Data Science

Background:

  • Online social networks exhibit endogenous (internal) and exogenous (external) event dynamics.
  • Controlling network activity requires understanding the impact of external influences.

Purpose of the Study:

  • To develop a method for quantifying and applying external drives to steer online social network activity.
  • To model and predict network behavior based on endogenous and exogenous event intensities.

Main Methods:

  • Modeling social events using multivariate Hawkes processes.
  • Deriving a linear relationship between exogenous event intensity and network activity.
  • Developing a convex optimization framework to determine optimal external drive levels.

Main Results:

  • A time-dependent linear relationship was established between exogenous event intensity and overall network activity.
  • The proposed convex optimization framework effectively determines the necessary external drive.
  • Empirical validation on Twitter data demonstrated superior accuracy in steering network activity compared to alternative methods.

Conclusions:

  • The study provides a principled framework for controlling online social network activity.
  • Accurate steering of network activity is achievable through carefully calibrated external drives.
  • The multivariate Hawkes process model offers a robust approach for analyzing complex social network dynamics.