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    <abstract>The spread of misinformation poses serious societal harms. While detection has been extensively studied, modeling user susceptibility remains a challenging yet important direction for strengthening users’ resilience. In this work, we formally define the Personalized Misinformation Susceptibility Prediction (MiSP) task, predicting whether a specific user is susceptible to (i.e., believe) a particular misinformation claim. We construct benchmark datasets derived from social science surveys and social-platform data, and design diverse evaluation settings covering different seen/unseen user–claim scenarios. Using this benchmark, we examine LLMs’ ability to simulate personalized susceptibility under realistic multi-attribute user profiles, and further evaluate LM-, LLM-, and GNN-based approaches, revealing their limitations. Motivated by these findings, we propose VulneraSpotter, which integrates semantic information and behavioral patterns via heterogeneous and homogeneous GNNs, enabling effective generalization to previously unseen users and claims. Extensive experiments demonstrate that VulneraSpotter outperforms existing baselines in predicting personalized susceptibility, uncovers meaningful group-level patterns, and shows promising potential for mitigating misinformation spread. This work provides a benchmark and modeling foundation for user-centric misinformation understanding and mitigation.</abstract>
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%0 Conference Proceedings
%T Detection Is Not The End: Personalized Modeling of User Susceptibility to Misinformation
%A Qi, Peng
%A Yan, Zehong
%A Hsu, Wynne
%A Lee, Mong-Li
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F qi-etal-2026-detection
%X The spread of misinformation poses serious societal harms. While detection has been extensively studied, modeling user susceptibility remains a challenging yet important direction for strengthening users’ resilience. In this work, we formally define the Personalized Misinformation Susceptibility Prediction (MiSP) task, predicting whether a specific user is susceptible to (i.e., believe) a particular misinformation claim. We construct benchmark datasets derived from social science surveys and social-platform data, and design diverse evaluation settings covering different seen/unseen user–claim scenarios. Using this benchmark, we examine LLMs’ ability to simulate personalized susceptibility under realistic multi-attribute user profiles, and further evaluate LM-, LLM-, and GNN-based approaches, revealing their limitations. Motivated by these findings, we propose VulneraSpotter, which integrates semantic information and behavioral patterns via heterogeneous and homogeneous GNNs, enabling effective generalization to previously unseen users and claims. Extensive experiments demonstrate that VulneraSpotter outperforms existing baselines in predicting personalized susceptibility, uncovers meaningful group-level patterns, and shows promising potential for mitigating misinformation spread. This work provides a benchmark and modeling foundation for user-centric misinformation understanding and mitigation.
%U https://aclanthology.org/2026.emnlp-main.407/
%P 9365-9379

Markdown (Informal)

[Detection Is Not The End: Personalized Modeling of User Susceptibility to Misinformation](https://aclanthology.org/2026.emnlp-main.407/) (Qi et al., EMNLP 2026)

• Detection Is Not The End: Personalized Modeling of User Susceptibility to Misinformation (Qi et al., EMNLP 2026)

ACL

• Peng Qi, Zehong Yan, Wynne Hsu, and Mong-Li Lee. 2026. Detection Is Not The End: Personalized Modeling of User Susceptibility to Misinformation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9365–9379, Budapest, Hungary. Association for Computational Linguistics.