APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 1

MELODY: Analyzing the Message-Opinion Coevolution and the Messages’ Influence on Opinion Dynamics in Social Networks

Huisheng Wang, Tsinghua University, China, Zhanjiang Chen, Tsinghua University, China, H. Vicky Zhao, Tsinghua University, China
 
Suggested Citation
Huisheng Wang, Zhanjiang Chen and H. Vicky Zhao (2024), "MELODY: Analyzing the Message-Opinion Coevolution and the Messages’ Influence on Opinion Dynamics in Social Networks", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 1, e22. http://dx.doi.org/10.1561/116.20240018

Publication Date: 24 Sep 2024
© 2024 H. Wang, Z. Chen, and H. V. Zhao
 
Subjects
Control/Graph-theoretic models,  Dynamics and asymptotic behavior of networks,  Modeling and analysis,  Brownian models,  Simulation,  Public opinion
 
Keywords
Message evolutionopinion dynamicssocial networkstatistical analysis
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
The BBM Model 
The MELODY Model 
Simulation Results 
Real User Test 
Conclusions 
References 

Abstract

For effective guidance of agents’ opinions in social networks, it is important to understand how messages evolve and analyze their impact on agents’ opinions. Opinion dynamics model how agents influence each other’s opinions and how the entire network’s opinions evolve. In the literature, many works have used opinion dynamics to study the influence of messages on agents’ opinions. However, most works assume static messages or independence among messages at different times. Studies in mass media theory show that the message evolution process exhibits temporal continuity, randomness, and polarization features. In this work, we first propose the Bounded Brownian Message (BBM) model to describe the message evolution process, jointly considering the above features. We then combine the BBM model with the classic DeGroot opinion dynamics model and propose the Message EvoLution and Opinion DYnamics (MELODY) model to study the impact of message evolution on opinion dynamics. We theoretically analyze the probability distributions and statistics of messages and opinions and study how messages influence the agents’ steady-state opinions. Simulations and real user tests validate our analyses. This study is critical to a better understanding of how messages shape agents’ opinions in social networks and design effective mechanisms to guide agents’ opinions.

DOI:10.1561/116.20240018