用模拟用户行为研究德国选举期间社交平台政治讨论
Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany
- 基于历史对话与动机建模虚拟用户交互
- 结果显示历史背景显著影响生成内容，资源受限时参与度下降
- 适合研究网络舆论演化与算法干预效果的学者
社交媒体用户参与受历史背景、时间约束及奖励驱动互动的影响。本研究提出一种基于代理的仿真方法，模拟用户交互，考虑过往对话历史、动机与资源限制。利用德国推特上的政治讨论数据，微调AI模型生成帖子与回复，融入情感分析、讽刺检测和冒犯性分类。仿真采用短视最优响应模型来决定代理行为，基于预期回报进行决策。结果表明，历史背景对生成回应有显著影响，并揭示在不同约束条件下参与度的变化规律。
原文摘要 · Abstract (English)
User engagement on social media platforms is influenced by historical context, time constraints, and reward-driven interactions. This study presents an agent-based simulation approach that models user interactions, considering past conversation history, motivation, and resource constraints. Utilizing German Twitter data on political discourse, we fine-tune AI models to generate posts and replies, incorporating sentiment analysis, irony detection, and offensiveness classification. The simulation employs a myopic best-response model to govern agent behavior, accounting for decision-making based on expected rewards. Our results highlight the impact of historical context on AI-generated responses and demonstrate how engagement evolves under varying constraints.
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