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Enough but not too many: A bi-threshold model for behavioral diffusion.

Fahimeh Alipour1, Fedor Dokshin2, Zeinab Maleki1

  • 1Department of Electrical and Computer Engineering, Isfahan University of Technology, Khomeyni Shahr, Daneshgah e Sanati Hwy, Isfahan PG9G+39R, Isfahan, Iran.

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The bi-threshold model, unlike the linear threshold model, accurately predicts behavior adoption and abandonment. This new model offers significant improvements in understanding information diffusion on social media.

Keywords:
bi-threshold modelinformation diffusionlinear threshold modelsocial network

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Area of Science:

  • Social network analysis
  • Information diffusion dynamics
  • Computational social science

Background:

  • Behavioral diffusion is often modeled using the linear threshold model, where adoption occurs when a sufficient number of social contacts adopt.
  • Empirical observations suggest individuals may also abandon behaviors when too many close contacts exhibit them, a phenomenon not captured by linear models.

Purpose of the Study:

  • To empirically test the validity and predictive power of the bi-threshold model for behavioral diffusion.
  • To investigate the role of an upper threshold in triggering behavioral disadoption.

Main Methods:

  • Extended a decision-tree based algorithm to estimate heterogeneous thresholds within the bi-threshold model framework.
  • Applied the bi-threshold and linear threshold models to analyze user engagement with news on social media across three distinct topics.

Main Results:

  • The bi-threshold model demonstrated predictive accuracy orders of magnitude greater than the linear threshold model for user engagement.
  • Performance gains were primarily attributed to the bi-threshold model's superior ability to predict behavioral decline.

Conclusions:

  • The study confirms the existence of an upper threshold influencing information diffusion in certain social media contexts.
  • The findings suggest that the bi-threshold mechanism may be relevant for understanding decision-making in various diffusion scenarios.