[2602.04674] Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility

Computer Science > Social and Information Networks arXiv:2602.04674 (cs) [Submitted on 4 Feb 2026 (v1), last revised 10 Apr 2026 (this version, v2)] Title:Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility Authors:Eun Cheol Choi, Lindsay E. Young, Emilio Ferrara View a PDF of the paper titled Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility, by Eun Cheol Choi and 2 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reproduce patterns of susceptibility to misinformation remains unclear. We test whether LLM-simulated survey respondents, prompted with participant profiles drawn from social survey data measuring network, demographic, attitudinal and behavioral features, can reproduce human patterns of misinformation belief and sharing. Using three online surveys as baselines, we evaluate whether LLM outputs match observed response distributions and recover feature-outcome associations present in the original survey data. LLM-generated responses capture broad distributional tendencies and show modest correlation with human responses, but consistently overstate the association between belief and sharing. Linear models fit to simulated responses exhibit substantially higher explained variance and place disproportionate weight on attitudinal and behaviora...