@inproceedings{borah-etal-2026-beliefsim,
title = "{BELIEFSIM}: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility",
author = "Borah, Angana and
Khan, Zohaib and
Mihalcea, Rada and
Perez-Rosas, Veronica",
editor = "Xu, Shanshan and
T.Y.S.S., Santosh and
Kurniawan, Kemal and
Peng, Siyao and
Wallbridge, Sarenne and
Koto, Fajri and
Dziri, Nouha and
Plank, Barbara",
booktitle = "Proceedings of the First Workshop on Pluralistic {AI} {NLP}: Diversity-aware, Sociotechnical, Responsible Alignment ({PANDORA} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.pandora-1.20/",
pages = "348--372",
ISBN = "979-8-89176-484-2",
abstract = "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 \textit{beliefs} as a primary driving factor. We introduce BeliefSim, a simulation framework that constructs survey-derived, demographic-group belief priors organized using a psychology-informed taxonomy. 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 datasets PANDORA and MIST, belief priors improve susceptibility alignment, with larger gains on PANDORA. Finally, post-training adaptation reaches 92.4{\%} accuracy and improves cross-participant generalization."
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<abstract>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 survey-derived, demographic-group belief priors organized using a psychology-informed taxonomy. 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 datasets PANDORA and MIST, belief priors improve susceptibility alignment, with larger gains on PANDORA. Finally, post-training adaptation reaches 92.4% accuracy and improves cross-participant generalization.</abstract>
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%0 Conference Proceedings
%T BELIEFSIM: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility
%A Borah, Angana
%A Khan, Zohaib
%A Mihalcea, Rada
%A Perez-Rosas, Veronica
%Y Xu, Shanshan
%Y T.Y.S.S., Santosh
%Y Kurniawan, Kemal
%Y Peng, Siyao
%Y Wallbridge, Sarenne
%Y Koto, Fajri
%Y Dziri, Nouha
%Y Plank, Barbara
%S Proceedings of the First Workshop on Pluralistic AI NLP: Diversity-aware, Sociotechnical, Responsible Alignment (PANDORA 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-484-2
%F borah-etal-2026-beliefsim
%X 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 survey-derived, demographic-group belief priors organized using a psychology-informed taxonomy. 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 datasets PANDORA and MIST, belief priors improve susceptibility alignment, with larger gains on PANDORA. Finally, post-training adaptation reaches 92.4% accuracy and improves cross-participant generalization.
%U https://aclanthology.org/2026.pandora-1.20/
%P 348-372
Markdown (Informal)
[BELIEFSIM: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility](https://aclanthology.org/2026.pandora-1.20/) (Borah et al., PANDORA 2026)
ACL