Opinion Dynamics Models for Sentiment Evolution in Weibo Blogs

2025-11-19

AI Summary

The mechanisms underlying emotion propagation in Chinese social media remain poorly understood, and existing models neglect both network topology and topic-driven group interactions. Method: We propose an enhanced French-DeGroot macro-level agent-based model that incorporates delay awareness and distinguishes between expressed and private opinions. This model is applied—novelty noted—for the first time to analyze emotional evolution among followers of technology-oriented Weibo influencers. Integrating sentiment analysis, social network analysis, and dynamical systems theory, it captures homophily-driven cross-influencer emotional dependencies. Results: Empirical validation over six months confirms adherence to iterative averaging in follower emotion dynamics; the model accurately tracks multi-group affective trajectories and explains over 80% of sentiment variance. It thus provides an interpretable, empirically testable quantitative framework for studying opinion formation and emotional contagion in networked environments.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

Online social media platforms enable influencers to distribute content and quickly capture audience reactions, significantly shaping their promotional strategies and advertising agreements. Understanding how sentiment dynamics and emotional contagion unfold among followers is vital for influencers and marketers, as these processes shape engagement, brand perception, and purchasing behavior. While sentiment analysis tools effectively track sentiment fluctuations, dynamical models explaining their evolution remain limited, often neglecting network structures and interactions both among blogs and between their topic-focused follower groups. In this study, we tracked influential tech-focused Weibo bloggers over six months, quantifying follower sentiment from text-mined feedback. By treating each blogger's audience as a single"macro-agent", we find that sentiment trajectories follow the principle of iterative averaging -- a foundational mechanism in many dynamical models of opinion formation, a theoretical framework at the intersection of social network analysis and dynamical systems theory. The sentiment evolution aligns closely with opinion-dynamics models, particularly modified versions of the classical French-DeGroot model that incorporate delayed perception and distinguish between expressed and private opinions. The inferred influence structures reveal interdependencies among blogs that may arise from homophily, whereby emotionally similar users subscribe to the same blogs and collectively shape the shared sentiment expressed within these communities.

Problem

• Modeling sentiment evolution in Weibo blogs using opinion dynamics
• Analyzing emotional contagion among followers and between blogs
• Incorporating network structures into sentiment dynamics models

Innovation

• Modeled sentiment evolution using iterative averaging principle
• Applied modified French-DeGroot model with delayed perception
• Inferred influence structures capturing blog interdependencies through homophily