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        <title>PsyScam: A Benchmark for Psychological Techniques in Real-World Scams</title>
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    <abstract>Over the years, online scams have grown dramatically,with nearly 50% of global consumersencountering scam attempts each week.These scams cause not only significant financiallosses to individuals and businesses, butalso lasting psychological trauma, largely dueto scammers’ strategic employment of psychologicaltechniques (PTs) to manipulate victims.Meanwhile, scammers continually evolve theirtactics by leveraging advances in Large LanguageModels (LLMs) to generate diverse scamvariants that easily bypass existing defenses.To address this pressing problem, we introducePsyScam, a benchmark designed to systematicallycapture the PTs employed in real-worldscam reports, and investigate how LLMs canbe utilized to generate variants of scams basedon the PTs and the contexts provided by thesescams. Specifically, we collect a wide range ofscam reports and ground its annotations of employedPTs in well-established cognitive andpsychological theories. We further demonstrateLLMs’ capabilities in generating through twodownstream tasks: scam completion, and scamaugmentation. Experimental results show thatPsyScam presents significant challenges toexisting models in both detecting and generatingscam content based on the PTs used byreal-world scammers. Our code and dataset areavailable.</abstract>
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%0 Conference Proceedings
%T PsyScam: A Benchmark for Psychological Techniques in Real-World Scams
%A Ma, Shang
%A Ma, Tianyi
%A Liu, Jiahao
%A Song, Wei
%A Liang, Zhenkai
%A Xiao, Xusheng
%A Ye, Yanfang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F ma-etal-2025-psyscam
%X Over the years, online scams have grown dramatically,with nearly 50% of global consumersencountering scam attempts each week.These scams cause not only significant financiallosses to individuals and businesses, butalso lasting psychological trauma, largely dueto scammers’ strategic employment of psychologicaltechniques (PTs) to manipulate victims.Meanwhile, scammers continually evolve theirtactics by leveraging advances in Large LanguageModels (LLMs) to generate diverse scamvariants that easily bypass existing defenses.To address this pressing problem, we introducePsyScam, a benchmark designed to systematicallycapture the PTs employed in real-worldscam reports, and investigate how LLMs canbe utilized to generate variants of scams basedon the PTs and the contexts provided by thesescams. Specifically, we collect a wide range ofscam reports and ground its annotations of employedPTs in well-established cognitive andpsychological theories. We further demonstrateLLMs’ capabilities in generating through twodownstream tasks: scam completion, and scamaugmentation. Experimental results show thatPsyScam presents significant challenges toexisting models in both detecting and generatingscam content based on the PTs used byreal-world scammers. Our code and dataset areavailable.
%R 10.18653/v1/2025.findings-emnlp.675
%U https://aclanthology.org/2025.findings-emnlp.675/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.675
%P 12623-12637

Markdown (Informal)

[PsyScam: A Benchmark for Psychological Techniques in Real-World Scams](https://aclanthology.org/2025.findings-emnlp.675/) (Ma et al., Findings 2025)

• PsyScam: A Benchmark for Psychological Techniques in Real-World Scams (Ma et al., Findings 2025)

ACL

• Shang Ma, Tianyi Ma, Jiahao Liu, Wei Song, Zhenkai Liang, Xusheng Xiao, and Yanfang Ye. 2025. PsyScam: A Benchmark for Psychological Techniques in Real-World Scams. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 12623–12637, Suzhou, China. Association for Computational Linguistics.