PersuaRealSim: Simulation Data and Trained Models for Bot-Driven Belief Manipulation

This record accompanies the Master’s thesis “Influencing Belief in LLM-Based Agent Networks: An Empirically Validated Simulation of Bot-Driven Manipulation” written by Julian Burmester.

The archive contains data and model artifacts produced with PersuaRealSim, a Twitter-like generative social simulation developed to study belief change under empirically grounded conditions. The simulation models interactions between human-like generative agents and specialised social-bot agents, while belief updating is externalised to a supervised persuasion judge rather than driven by unconstrained prompting or heuristic rules.

Specifically, this record includes

1. Raw outputs of 36 simulation runs conducted across two misinformation-relevant domains, capturing timelines, belief trajectories, and derived evaluation data.

2. The final trained RankFormer persuasion-judge model, trained on 46,846 r/ChangeMyView threads with human-verified belief change outcomes and used to estimate message-level persuasion effects in the simulation.

The RankFormer outputs are calibrated to plausible stance-shift magnitudes and injected into a continuous belief update mechanism, enabling the analysis of bot-driven persuasion dynamics under empirically constrained assumptions.

The corresponding source code for the simulation environment (PersuaRealSim), agent implementations, and analysis scripts is available at
https://github.com/JulianBurmester/PersuaRealSim