Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany

Published on Mar 31

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Abstract

The study simulates user interactions on Twitter using AI models fine-tuned with sentiment analysis, irony detection, and offensiveness classification, showing how historical context and reward-driven behavior affect engagement.

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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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