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.

翻译：社交媒体平台上的用户参与度受到历史背景、时间限制和奖励驱动互动的影响。本研究提出一种基于智能体的模拟方法，通过考虑过往对话历史、动机和资源约束来建模用户交互。利用德国推特平台上政治话语数据，我们微调AI模型以生成帖子和回复，并整合了情感分析、反讽检测和冒犯性分类功能。该模拟采用短视最优响应模型来调控智能体行为，考量基于预期奖励的决策机制。研究结果凸显了历史背景对AI生成回复的影响，并展示了在不同约束条件下参与度如何动态演变。