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Personality Modeling for Persuasion of Misinformation using AI Agent

Qianmin Lou, Wentao Xu

TL;DR

The paper investigates how Big Five personality traits influence persuasion and resistance to misinformation using an agent-based approach. Six agents with distinct Extraversion, Agreeableness, and Neuroticism profiles interact across six misinformation topics within the AgentScope framework, guided by the GLM-4-Flash model, producing 90 interactions. Key findings show that non-aggressive, trust-based persuasion can be as or more effective than confrontational approaches, and that trait combinations yield non-transitive, topic-dependent dynamics, including a 59.4% HIV-persuasion rate for a critical–sensitive pair. These insights advance both theoretical understanding and practical design of personality-aware interventions to counter misinformation in social media contexts.

Abstract

The proliferation of misinformation on social media platforms has highlighted the need to understand how individual personality traits influence susceptibility to and propagation of misinformation. This study employs an innovative agent-based modeling approach to investigate the relationship between personality traits and misinformation dynamics. Using six AI agents embodying different dimensions of the Big Five personality traits (Extraversion, Agreeableness, and Neuroticism), we simulated interactions across six diverse misinformation topics. The experiment, implemented through the AgentScope framework using the GLM-4-Flash model, generated 90 unique interactions, revealing complex patterns in how personality combinations affect persuasion and resistance to misinformation. Our findings demonstrate that analytical and critical personality traits enhance effectiveness in evidence-based discussions, while non-aggressive persuasion strategies show unexpected success in misinformation correction. Notably, agents with critical traits achieved a 59.4% success rate in HIV-related misinformation discussions, while those employing non-aggressive approaches maintained consistent persuasion rates above 40% across different personality combinations. The study also revealed a non-transitive pattern in persuasion effectiveness, challenging conventional assumptions about personality-based influence. These results provide crucial insights for developing personality-aware interventions in digital environments and suggest that effective misinformation countermeasures should prioritize emotional connection and trust-building over confrontational approaches. The findings contribute to both theoretical understanding of personality-misinformation dynamics and practical strategies for combating misinformation in social media contexts.

Personality Modeling for Persuasion of Misinformation using AI Agent

TL;DR

The paper investigates how Big Five personality traits influence persuasion and resistance to misinformation using an agent-based approach. Six agents with distinct Extraversion, Agreeableness, and Neuroticism profiles interact across six misinformation topics within the AgentScope framework, guided by the GLM-4-Flash model, producing 90 interactions. Key findings show that non-aggressive, trust-based persuasion can be as or more effective than confrontational approaches, and that trait combinations yield non-transitive, topic-dependent dynamics, including a 59.4% HIV-persuasion rate for a critical–sensitive pair. These insights advance both theoretical understanding and practical design of personality-aware interventions to counter misinformation in social media contexts.

Abstract

The proliferation of misinformation on social media platforms has highlighted the need to understand how individual personality traits influence susceptibility to and propagation of misinformation. This study employs an innovative agent-based modeling approach to investigate the relationship between personality traits and misinformation dynamics. Using six AI agents embodying different dimensions of the Big Five personality traits (Extraversion, Agreeableness, and Neuroticism), we simulated interactions across six diverse misinformation topics. The experiment, implemented through the AgentScope framework using the GLM-4-Flash model, generated 90 unique interactions, revealing complex patterns in how personality combinations affect persuasion and resistance to misinformation. Our findings demonstrate that analytical and critical personality traits enhance effectiveness in evidence-based discussions, while non-aggressive persuasion strategies show unexpected success in misinformation correction. Notably, agents with critical traits achieved a 59.4% success rate in HIV-related misinformation discussions, while those employing non-aggressive approaches maintained consistent persuasion rates above 40% across different personality combinations. The study also revealed a non-transitive pattern in persuasion effectiveness, challenging conventional assumptions about personality-based influence. These results provide crucial insights for developing personality-aware interventions in digital environments and suggest that effective misinformation countermeasures should prioritize emotional connection and trust-building over confrontational approaches. The findings contribute to both theoretical understanding of personality-misinformation dynamics and practical strategies for combating misinformation in social media contexts.
Paper Structure (12 sections, 2 figures, 3 tables)

This paper contains 12 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. The Role of Personality Traits in Belief in Fake News
  4. Research on Agent-Based Misinformation Propagation
  5. Methodology
  6. Experiment Settings
  7. Results
  8. Personality-Driven Dynamics in Misinformation Interactions
  9. Effectiveness of Non-Aggressive Persuasion Strategies in Misinformation Discourse
  10. Discussion and Conclusion
  11. Limitations and Future Research
  12. Implications and Conclusions

Figures (2)

  • Figure 1: Comparative Analysis of Agent 1 's Interaction Outcomes with Agents 2, 4, and 6: Success Rates (47.5%, 33.2%, 40.4%), Failure Rates (22.3%, 24.4%, 23.9%), and Draw Rates (30.1%, 42.4%, 35.8%).
  • Figure 2: Comparative Analysis of Agent 3 's Interaction Outcomes with Agents 2, 4, and 6: Success Rates (49.5%, 38.6%, 36.3%), Failure Rates (21.0%, 23.7%, 24.5%), and Draw Rates (29.5%, 37.7%, 39.2%).