Related Experiment Video
Updated: Jul 18, 2025
12:21
Stimulated Stokes and Antistokes Raman Scattering in Microspherical Whispering Gallery Mode Resonators
Published on: April 4, 2016
11.3K
Stochastic Stabilization of Dual-Layer Rumor Propagation Model with Multiple Channels and Rumor-Detection Mechanism
Xiaojing Zhong1, Chaolong Luo1, Xiaowu Dong1
1School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
Entropy (Basel, Switzerland)
|August 26, 2023
Summary
This study introduces a dual-layer model to understand how individual perceptions influence rumor spread across multiple media channels. It proposes a novel noise stabilization method to effectively control and suppress the propagation of misinformation.
Area of Science:
- Information Science
- Computational Social Science
- Mathematical Modeling
Background:
- Information technology enables rapid dissemination of rumors through diverse channels.
- Discrepancies between media information and individual cognition significantly impact rumor propagation dynamics.
- Understanding rumor spread is crucial in the digital age.
Purpose of the Study:
- To establish a dual-layer rumor propagation model that accounts for individual cognitive differences.
- To analyze rumor propagation behavior across multiple information channels.
- To develop and evaluate methods for rumor suppression.
Main Methods:
- Developed a dual-layer mathematical model for rumor propagation.
- Analyzed equilibrium points and their stability to determine rumor persistence thresholds.
- Designed and implemented non-periodic intermittent noise stabilization techniques.
- Validated theoretical findings through experimental simulations.
Main Results:
- Identified a threshold for rumor disappearance or persistence, influenced by media numbers and debunkers.
- Demonstrated the effectiveness of the proposed noise stabilization method in controlling rumor spread.
- Provided specific expressions for control intensity in the suppression strategy.
- Confirmed the accuracy of the model and control methods via simulations.
Conclusions:
- Individual cognition plays a critical role in rumor propagation across multiple channels.
- The developed model accurately predicts rumor dynamics and provides insights into spread.
- Non-periodic intermittent noise stabilization offers an effective strategy for combating misinformation.
- This research contributes to understanding and mitigating the impact of rumors in digital environments.
Related Concept Videos
00:59Propagation of Uncertainty from Random Error
726
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
726
01:10Propagation of Uncertainty from Systematic Error
554
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
554
01:24BIBO stability of continuous and discrete -time systems
433
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
433
01:15Theories of Dissolution: Diffusion Layer Model
809
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
809
01:18Second Order systems II
130
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
130
01:25Multimachine Stability
188
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
188