Build An Influential Bot In Social Media Simulations With Large Language Models

2024-11-29

arXiv.org

AI Summary

This study addresses three key challenges in modeling public opinion dynamics on online social platforms: (1) insufficient understanding of opinion evolution mechanisms, (2) scarcity of authentic influence data, and (3) ethical constraints inherent in traditional empirical analysis methods. To overcome these, we propose a novel simulation framework integrating large language models (LLMs) with agent-based modeling (ABM). Within this framework, agents autonomously generate content, evolve opinions, and dynamically reconfigure their attention networks. Crucially, we introduce proximal policy optimization (PPO)—a deep reinforcement learning algorithm—into the modeling of emergent opinion leadership for the first time. We further design a constrained action space and a self-observation mechanism to ensure policy stability and interpretability. Experimental results demonstrate robust, spontaneous emergence of opinion leaders across diverse scenarios. Learning curves confirm that the model autonomously discovers optimal influence strategies, exhibiting both adaptability and convergence in uncertain, dynamic information environments.

Technology Category

Multiagent Systems: Agent-Based Simulation and Emergent BehaviorSearch and Optimization: Learning to SearchCognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarizationUser Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalizationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Understanding the dynamics of public opinion evolution on online social platforms is critical for analyzing influence mechanisms. Traditional approaches to influencer analysis are typically divided into qualitative assessments of personal attributes and quantitative evaluations of influence power. In this study, we introduce a novel simulated environment that combines Agent-Based Modeling (ABM) with Large Language Models (LLMs), enabling agents to generate posts, form opinions, and update follower networks. This simulation allows for more detailed observations of how opinion leaders emerge. Additionally, we present an innovative application of Reinforcement Learning (RL) to replicate the process of opinion leader formation. Our findings reveal that limiting the action space and incorporating self-observation are key factors for achieving stable opinion leader generation. The learning curves demonstrate the model's capacity to identify optimal strategies and adapt to complex, unpredictable dynamics.

Problem

• Ethical experimentation on social media influence mechanisms
• Simulating opinion evolution using LLM-based agentic reinforcement learning
• Identifying key factors for stable opinion leader generation

Innovation

• Agentic LLM bots simulate social influence dynamics
• Reinforcement Learning optimizes linguistic interaction strategies
• Constrained action space ensures stable opinion leadership