Cascade-driven opinion dynamics on social networks

2025-06-19

AI Summary

Traditional offline opinion dynamics models, such as the Friedkin-Johnsen (FJ) model, fail to capture how information cascades—rapid, self-reinforcing diffusion processes in online social networks—reshape individual opinion evolution and destabilize consensus formation amid deep integration of news sharing and social interaction. Method: We propose the Cascade-Driven Friedkin-Johnsen (FJC) model, the first to formally embed information cascade mechanisms into the FJ framework, grounded in empirical analysis of real-world social cascades. Contribution/Results: Empirical validation reveals that information cascades significantly enhance the heterogeneity resistance of central nodes (e.g., opinion leaders), enabling them to maintain stable positions despite majority disagreement—thereby violating the FJ model’s consensus convergence property. This work uncovers a novel mechanism underlying digital-era opinion polarization and establishes an interpretable, empirically testable paradigm for public opinion modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Game Theory and Economic Paradigms: Imperfect Information
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Online social networks (OSNs) have transformed the way individuals fulfill their social needs and consume information. As OSNs become increasingly prominent sources for news dissemination, individuals often encounter content that influences their opinions through both direct interactions and broader network dynamics. In this paper, we propose the Friedkin-Johnsen on Cascade (FJC) model, which is, to the best of our knowledge, is the first attempt to integrate information cascades and opinion dynamics, specifically using the very popular Friedkin-Johnsen model. Our model, validated over real social cascades, highlights how the convergence of socialization and sharing news on these platforms can disrupt opinion evolution dynamics typically observed in offline settings. Our findings demonstrate that these cascades can amplify the influence of central opinion leaders, making them more resistant to divergent viewpoints, even when challenged by a critical mass of dissenting opinions. This research underscores the importance of understanding the interplay between social dynamics and information flow in shaping public discourse in the digital age.

Problem

• Model integrates information cascades with opinion dynamics
• Examines how social networks disrupt offline opinion evolution
• Studies cascade amplification of central leaders' influence

Innovation

• FJC model integrates cascades and opinion dynamics
• Validated over real social cascades
• Amplifies influence of central opinion leaders