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Related Experiment Video

Updated: Sep 21, 2025

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

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Published on: May 14, 2019

From subcritical behavior to a correlation-induced transition in rumor models.

Guilherme Ferraz de Arruda¹, Lucas G S Jeub², Angélica S Mata³

¹ISI Foundation, Via Chisola 5, 10126, Torino, Italy. gui.f.arruda@gmail.com.

Nature Communications

|June 1, 2022

Summary

This study reveals a continuous phase transition in the Maki-Thompson rumor spreading model, contrary to prior beliefs. A modified model with forgetting shows rumor lifespan increases as spreading rate drops.

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Last Updated: Sep 21, 2025

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

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Published on: May 14, 2019

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Area of Science:

Complex Systems
Mathematical Modeling
Information Science

Background:

Human interactions drive information and rumor spread, amplified by digital communication.
The Maki-Thompson model is a key mathematical framework for understanding rumor dynamics.
Previous analyses suggested the Maki-Thompson model lacks a phase transition.

Purpose of the Study:

To investigate the presence of a phase transition in the Maki-Thompson model.
To explore a modified Maki-Thompson model incorporating a forgetting mechanism.
To analyze the impact of forgetting on rumor dynamics and lifespan.

Main Methods:

Mathematical analysis using mean-field approximations.
Development and simulation of a modified Maki-Thompson model with a forgetting component.
Characterization of rumor dynamics using analytic and numeric methods.

Main Results:

A continuous phase transition was identified in the standard Maki-Thompson model.
The modified model with forgetting exhibits rich dynamic behaviors.
Subcritical rumor behavior shows a power-law relationship between lifespan and spreading rate.

Conclusions:

The Maki-Thompson model exhibits a richer dynamic behavior than previously understood.
The inclusion of a forgetting mechanism significantly alters rumor spreading dynamics.
This research provides new insights into the complex nature of information diffusion.