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TRUVACE RECORD VERSION
record: TRV-2026-2527
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:48:31.356273Z
status: published
lens: trace
sector: crime
headline: Large Language Models Can Assist in Generating Personality-Matched Anti-Fraud Voice Warnings in the Chinese Context
dek: With the rise of AI-generated content, personalized interventions have become increasingly feasible in high-risk decision-making contexts such as online fraud. This research examined whether large language models (LLMs), exemplified by DeepSeek, can generate personality-matched voice warnings to enhance anti-fraud persuasion in the Chinese context. Across two studies, warnings were tailored to Big Five personality traits by manipulating either text content only (Study 1, N = 296) or both text and vocal parameter…
gain_title: In a Chinese study of 296 people, anti-fraud warnings that DeepSeek tailored to personality traits were rated significantly more effective than unmatched ones, most clearly for Openness.
problem_title: In a second Chinese study of 191 people, personality-matched warnings showed no significant advantage when DeepSeek generated both the text and the vocal parameters.
trace_subject: AI-generated personality-matched anti-fraud warnings in China
gain_reading: In a Chinese study of 296 people, anti-fraud warnings that DeepSeek tailored to personality traits were rated significantly more effective than unmatched ones, most clearly for Openness.
gain_evidence: personality-matched warnings significantly increased perceived warning effectiveness, with the clearest effect for Openness
problem_reading: In a second Chinese study of 191 people, personality-matched warnings showed no significant advantage when DeepSeek generated both the text and the vocal parameters.
problem_evidence: Study 2 found no significant personality-matching effects when both text and vocal parameters were generated by the LLM
quick_read: Researchers tested whether the large language model DeepSeek can write anti-fraud warnings matched to a person's Big Five personality traits. In Study 1, with 296 participants, text-only matched warnings were rated as significantly more effective, and the effect was clearest for Openness.

Study 2, with 191 participants, extended the approach by having the model also set vocal parameters for spoken warnings. It found no significant personality-matching effects, so the benefit seen in text did not carry over to voice.

The outcome measured was perceived warning effectiveness, not whether people actually avoided fraud. The authors conclude the models may support personality-based warning generation under controlled text-only conditions, while effectiveness remains limited for multidimensional voice-warning design.
limitation: The studies measured perceived warning effectiveness, and the personality-matching benefit disappeared when the model also generated vocal parameters.
tag: Dual reading
key_points: Study 1 enrolled 296 participants and varied only the text content of warnings by Big Five personality trait. | Personality-matched text warnings significantly increased perceived warning effectiveness in Study 1. | The clearest matching effect in Study 1 was for the Openness trait. | Study 2 enrolled 191 participants and had DeepSeek generate both text and vocal parameters. | Study 2 found no significant personality-matching effects for the combined text and voice warnings. | The work was set in the Chinese context and used DeepSeek as the example model.
rundown: The research asks whether AI-generated content can make personalised interventions practical in high-risk decisions such as online fraud. DeepSeek was used to generate warnings matched to Big Five personality traits, tested in the Chinese context.

Study 1 manipulated only the text of the warnings among 296 participants. Matched warnings raised perceived effectiveness significantly, with Openness showing the clearest effect.

Study 2 added vocal parameters among 191 participants, with the model generating both text and voice settings. No significant matching effects appeared, which the authors read as limited effectiveness for multidimensional voice-warning design.
sources:
- peer_reviewed | International Journal of Human–Computer Interaction | https://doi.org/10.1080/10447318.2026.2674290 | 2026-05-28
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