Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 31 Mar 2025.
Builds on
7 of 23 references.
References · 7
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
- 2023User Behavior Simulation with Large Language Model based Agentscited by 140
- 2025Simulating Rumor Spreading in Social Networks using LLM Agentscited by 16
- 2025A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responsescited by 8
- 2015Measuring user influence on Twitter: A surveycited by 7
- 2025Performant LLM Agentic Framework for Conversational AIcited by 3
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