说服
维数(图论)
匹配(统计)
比例(比率)
政治
计算机科学
说服性沟通
个性化医疗
意识形态
认知心理学
心理学
社会心理学
医学
生物信息学
政治学
量子力学
生物
物理
病理
数学
法学
纯数学
作者
Sandra Matz,Jacob D. Teeny,Sumer S. Vaid,H. Peters,Gabriella M. Harari,Moran Cerf
标识
DOI:10.1038/s41598-024-53755-0
摘要
Matching the language or content of a message to the psychological profile of its recipient (known as "personalized persuasion") is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency. We discuss the implications for researchers, practitioners, and the general public.
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