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Large Language Models Can Assist in Generating Personality-Matched Anti-Fraud Voice Warnings in the Chinese Context
- Truvace record
- TRV-2026-2527raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
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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…
57Gain side
How the two sides scoreClose call
59Problem side
The gain
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.
The problem
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.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
The studies measured perceived warning effectiveness, and the personality-matching benefit disappeared when the model also generated vocal parameters.
Sources
1 on file- International Journal of Human–Computer Interactiondoi:10.1080/10447318.2026.2674290
Cite this record
v1Truvace Impact Record TRV-2026-2527, v1: “Large Language Models Can Assist in Generating Personality-Matched Anti-Fraud Voice Warnings in the Chinese Context.” Truvace, 2026-11-12. /record/TRV-2026-2527 (accessed on the day you copy it). sha256 d93e9cfdbe172d9c…
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