Simulating Misinformation Vulnerabilities With Agent Personas
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912692818673664 |
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| author | Farr, David Ng, Lynnette Hui Xian Prochaska, Stephen Cruickshank, Iain J. West, Jevin |
| author_facet | Farr, David Ng, Lynnette Hui Xian Prochaska, Stephen Cruickshank, Iain J. West, Jevin |
| contents | Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effective interventions, yet real-world experimentation is impractical and ethically challenging. To address this, we develop an agent-based simulation using Large Language Models (LLMs) to model responses to misinformation. We construct agent personas spanning five professions and three mental schemas, and evaluate their reactions to news headlines. Our findings show that LLM-generated agents align closely with ground-truth labels and human predictions, supporting their use as proxies for studying information responses. We also find that mental schemas, more than professional background, influence how agents interpret misinformation. This work provides a validation of LLMs to be used as agents in an agent-based model of an information network for analyzing trust, polarization, and susceptibility to deceptive content in complex social systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04697 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulating Misinformation Vulnerabilities With Agent Personas Farr, David Ng, Lynnette Hui Xian Prochaska, Stephen Cruickshank, Iain J. West, Jevin Social and Information Networks Artificial Intelligence Computation and Language Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effective interventions, yet real-world experimentation is impractical and ethically challenging. To address this, we develop an agent-based simulation using Large Language Models (LLMs) to model responses to misinformation. We construct agent personas spanning five professions and three mental schemas, and evaluate their reactions to news headlines. Our findings show that LLM-generated agents align closely with ground-truth labels and human predictions, supporting their use as proxies for studying information responses. We also find that mental schemas, more than professional background, influence how agents interpret misinformation. This work provides a validation of LLMs to be used as agents in an agent-based model of an information network for analyzing trust, polarization, and susceptibility to deceptive content in complex social systems. |
| title | Simulating Misinformation Vulnerabilities With Agent Personas |
| topic | Social and Information Networks Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.04697 |