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A Novel Dynamic Epidemic Model for Successive Opinion Diffusion in Social Networks

Bin Han, Fabienne Renckens, C. Clark Cao, Hans D. Schotten

TL;DR

The paper addresses how successive rumors shape opinion diffusion and polarization in social networks, addressing limitations of static-network diffusion models and single-rumor approaches. It introduces a dynamic SHIMR framework that tracks per-rumor statuses, non-linear opinion diffusion, homophily-driven social weights, and sociocognitive decision making, enabling accumulation of opinion from correlated rumors and feedback on network structure. Key contributions include exposure modeling, backfire and self-presentation effects, and a memory-aware diffusion rule, all validated through simulations that reproduce echo-chamber formation and network evolution, with sensitivity analyses across parameters and influencer configurations. The findings offer insights into how polarization emerges and persists, and they suggest strategies for polarization mitigation and network-interaction design, with avenues for real-world data validation in future work.

Abstract

This paper proposes a dynamic epidemic model for successive opinion diffusion in social networks, extending the SHIMR model. It incorporates dynamic decision-making influenced by social distances and captures accumulative opinion diffusion caused by interrelated rumors. The model reflects the impact of rumor spread on social network structures. Simulations validate its effectiveness in explaining phenomena like the echo chamber effect and provide insights into opinion diffusion dynamics, with implications for understanding social polarization and network evolution.

A Novel Dynamic Epidemic Model for Successive Opinion Diffusion in Social Networks

TL;DR

The paper addresses how successive rumors shape opinion diffusion and polarization in social networks, addressing limitations of static-network diffusion models and single-rumor approaches. It introduces a dynamic SHIMR framework that tracks per-rumor statuses, non-linear opinion diffusion, homophily-driven social weights, and sociocognitive decision making, enabling accumulation of opinion from correlated rumors and feedback on network structure. Key contributions include exposure modeling, backfire and self-presentation effects, and a memory-aware diffusion rule, all validated through simulations that reproduce echo-chamber formation and network evolution, with sensitivity analyses across parameters and influencer configurations. The findings offer insights into how polarization emerges and persists, and they suggest strategies for polarization mitigation and network-interaction design, with avenues for real-world data validation in future work.

Abstract

This paper proposes a dynamic epidemic model for successive opinion diffusion in social networks, extending the SHIMR model. It incorporates dynamic decision-making influenced by social distances and captures accumulative opinion diffusion caused by interrelated rumors. The model reflects the impact of rumor spread on social network structures. Simulations validate its effectiveness in explaining phenomena like the echo chamber effect and provide insights into opinion diffusion dynamics, with implications for understanding social polarization and network evolution.

Paper Structure

This paper contains 16 sections, 9 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Conventional Epidemic Models
  3. The Extended SHIMR Model
  4. Correlated Rumors, Value, Opinion, and States
  5. Non-Linear Diffusion of Opinion
  6. Social Weight and Homophily
  7. Exposure to Rumors
  8. Sociocognitive Decision Making
  9. Self-Presentation-Driven Expression
  10. Popularity-Based Loss of Interest
  11. Overall Process
  12. Numerical Simulations
  13. Simulation Setup
  14. Observing the Echo Chamber Effect
  15. Sensitivity Tests
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Conventional SHIMR model for an $\left<i,j\right>$-node
  • Figure 2: Proposed model for a normal agent $n$ upon rumor $k$
  • Figure 3: Opinions of normal agents and the social relations among them, \ref{['subfig:echo_chamber_init']} before and\ref{['subfig:echo_chamber_example']} after 150 rounds of diffusion. The visualized distance between each two nodes is inversely proportional to the social weight between them.
  • Figure 4: Distributions of social weight and opinion after 150 rounds of diffusion.