# Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility

arXiv: https://arxiv.org/abs/2603.03585 · HTML: https://mlsanity.com/paper/2603.03585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 56 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [Virtual Personas for Language Models via an Anthology of Backstories](https://mlsanity.com/paper/2407.06576/) (2024)
- [Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions](https://mlsanity.com/paper/2410.02584/) (2024)
- [Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks](https://mlsanity.com/paper/2406.17232/) (2024)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas](https://mlsanity.com/paper/2406.14462/) (2024)
- [Mind the (Belief) Gap: Group Identity in the World of LLMs](https://mlsanity.com/paper/2503.02016/) (2025)
- [Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions](https://mlsanity.com/paper/2503.02038/) (2025)
- [On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook](https://mlsanity.com/paper/1808.09218/) (2018)
- [Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics](https://mlsanity.com/paper/2501.17273/) (2025)
- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [Using LLMs to Model the Beliefs and Preferences of Targeted Populations](https://mlsanity.com/paper/2403.20252/) (2024)
- [Evaluating the Simulation of Human Personality-Driven Susceptibility to Misinformation with LLMs](https://mlsanity.com/paper/2506.23610/) (2025)
- [Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths](https://mlsanity.com/paper/2506.02481/) (2025)

## Built on by (most cited first, 1 of 1)

- [You Can't Fool Us: Understanding the Resilience of LLM-driven Agent Communities to Misinformation](https://mlsanity.com/paper/2605.17353/) (2026)

## Abstract (arXiv)

Misinformation is a growing societal threat, and susceptibility to misinformative claims varies across demographic groups due to differences in underlying beliefs. As Large Language Models (LLMs) are increasingly used to simulate human behaviors, we investigate whether they can simulate demographic misinformation susceptibility, treating beliefs as a primary driving factor. We introduce BeliefSim, a simulation framework that constructs demographic belief profiles using psychology-informed misinformation taxonomies and survey priors. We study prompt-based conditioning and post-training adaptation, and conduct a multi-fold evaluation using: (i) susceptibility alignment and (ii) counterfactual demographic sensitivity. Across both datasets and modeling strategies, we show that beliefs provide a strong prior for simulating misinformation susceptibility, with alignment up to 92%.