• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

生成式人工智能在大规模个性化说服方面的潜力。

The potential of generative AI for personalized persuasion at scale.

机构信息

Columbia Business School, New York, USA.

Center for Advanced Technology and Human Performance, Columbia Business School, New York, USA.

出版信息

Sci Rep. 2024 Feb 26;14(1):4692. doi: 10.1038/s41598-024-53755-0.

DOI:10.1038/s41598-024-53755-0
PMID:38409168
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10897294/
Abstract

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.

摘要

将信息的语言或内容与接收者的心理特征相匹配(称为“个性化说服”)被广泛认为是最有效的信息传递策略之一。我们证明,像 ChatGPT 这样的大型语言模型的快速发展可以通过使个性化说服规模化来加速这种影响。在四项研究(包括七个子研究;总 N=1788)中,我们表明 ChatGPT 生成的个性化信息比非个性化信息具有更显著的影响力。这在不同的说服领域(例如,消费品营销、气候行动的政治呼吁)、心理特征(例如,个性特征、政治意识形态、道德基础)以及仅向语言模型提供一个简短的提示来命名或描述目标心理维度时都是如此。因此,我们的发现是首批证明大型语言模型有可能自动化、规模化地使用个性化说服的研究之一,从而提高其有效性和效率。我们讨论了这些发现对研究人员、从业者和公众的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/7d8eee6cc651/41598_2024_53755_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/7a34a187b2f4/41598_2024_53755_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/473ba943c792/41598_2024_53755_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/550cb6926207/41598_2024_53755_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/f0c7f0fccdc8/41598_2024_53755_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/65c297bac87e/41598_2024_53755_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/7d8eee6cc651/41598_2024_53755_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/7a34a187b2f4/41598_2024_53755_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/473ba943c792/41598_2024_53755_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/550cb6926207/41598_2024_53755_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/f0c7f0fccdc8/41598_2024_53755_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/65c297bac87e/41598_2024_53755_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c3/10897294/7d8eee6cc651/41598_2024_53755_Fig6_HTML.jpg

相似文献

1
The potential of generative AI for personalized persuasion at scale.生成式人工智能在大规模个性化说服方面的潜力。
Sci Rep. 2024 Feb 26;14(1):4692. doi: 10.1038/s41598-024-53755-0.
2
Evaluating the persuasive influence of political microtargeting with large language models.评估大型语言模型进行政治微目标推送的说服力影响。
Proc Natl Acad Sci U S A. 2024 Jun 11;121(24):e2403116121. doi: 10.1073/pnas.2403116121. Epub 2024 Jun 7.
3
The persuasive effects of political microtargeting in the age of generative artificial intelligence.生成式人工智能时代政治微观目标定位的说服效果。
PNAS Nexus. 2024 Jan 29;3(2):pgae035. doi: 10.1093/pnasnexus/pgae035. eCollection 2024 Feb.
4
Historical Change in the Moral Foundations of Political Persuasion.政治说服的道德基础的历史变迁。
Pers Soc Psychol Bull. 2020 Nov;46(11):1523-1537. doi: 10.1177/0146167220907467. Epub 2020 Mar 18.
5
Psychological targeting as an effective approach to digital mass persuasion.心理定位作为一种有效的数字大众说服方法。
Proc Natl Acad Sci U S A. 2017 Nov 28;114(48):12714-12719. doi: 10.1073/pnas.1710966114. Epub 2017 Nov 13.
6
Personalized persuasion: tailoring persuasive appeals to recipients' personality traits.个性化说服:根据接收者的个性特征调整说服诉求。
Psychol Sci. 2012 Jun;23(6):578-81. doi: 10.1177/0956797611436349. Epub 2012 Apr 30.
7
Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models.利用生成式人工智能辅助学习罕见且复杂的诊断:对流行的大型语言模型的定性研究。
JMIR Med Educ. 2024 Feb 13;10:e51391. doi: 10.2196/51391.
8
Framing Climate Change Impacts as Moral Violations: The Pathway of Perceived Message Credibility.将气候变化影响框定为道德违规行为:感知信息可信度的途径。
Int J Environ Res Public Health. 2022 Apr 25;19(9):5210. doi: 10.3390/ijerph19095210.
9
Evaluating the Influence of Role-Playing Prompts on ChatGPT's Misinformation Detection Accuracy: Quantitative Study.评估角色扮演提示对 ChatGPT 错误信息检测准确率的影响:定量研究。
JMIR Infodemiology. 2024 Sep 26;4:e60678. doi: 10.2196/60678.
10
Shifting liberal and conservative attitudes using moral foundations theory.运用道德基础理论转变自由主义和保守主义态度。
Pers Soc Psychol Bull. 2014 Dec;40(12):1559-73. doi: 10.1177/0146167214551152. Epub 2014 Oct 6.

引用本文的文献

1
The persuasive potential of AI-paraphrased information at scale.大规模人工智能释义信息的说服潜力。
PNAS Nexus. 2025 Jul 22;4(7):pgaf207. doi: 10.1093/pnasnexus/pgaf207. eCollection 2025 Jul.
2
Neural correlates of evaluative bias against artificial intelligence-labeled versus human-labeled artworks.针对人工智能标注与人类标注艺术品的评价偏差的神经关联。
Soc Cogn Affect Neurosci. 2025 Jan 18;20(1). doi: 10.1093/scan/nsaf071.
3
LLM-generated messages can persuade humans on policy issues.大语言模型生成的信息能够在政策问题上说服人类。

本文引用的文献

1
Large language models can infer psychological dispositions of social media users.大型语言模型可以推断社交媒体用户的心理倾向。
PNAS Nexus. 2024 Jun 13;3(6):pgae231. doi: 10.1093/pnasnexus/pgae231. eCollection 2024 Jun.
2
Quantifying the potential persuasive returns to political microtargeting.量化政治微目标定位的潜在说服力回报。
Proc Natl Acad Sci U S A. 2023 Jun 20;120(25):e2216261120. doi: 10.1073/pnas.2216261120. Epub 2023 Jun 12.
3
Leveraging psychological fit to encourage saving behavior.利用心理契合度来鼓励储蓄行为。
Nat Commun. 2025 Jul 1;16(1):6037. doi: 10.1038/s41467-025-61345-5.
4
On the conversational persuasiveness of GPT-4.论GPT-4的对话说服力。
Nat Hum Behav. 2025 May 19. doi: 10.1038/s41562-025-02194-6.
5
How AI sources can increase openness to opposing views.人工智能资源如何能够增强对反对观点的开放性。
Sci Rep. 2025 May 17;15(1):17170. doi: 10.1038/s41598-025-00791-z.
6
The "multiple exposure effect" (MEE): How multiple exposures to similarly biased online content can cause increasingly larger shifts in opinions and voting preferences.“多次曝光效应”(MEE):多次接触存在类似偏向性的网络内容如何导致观点和投票偏好发生越来越大的转变。
PLoS One. 2025 May 12;20(5):e0322900. doi: 10.1371/journal.pone.0322900. eCollection 2025.
7
Testing theories of political persuasion using AI.利用人工智能检验政治说服理论。
Proc Natl Acad Sci U S A. 2025 May 6;122(18):e2412815122. doi: 10.1073/pnas.2412815122. Epub 2025 May 2.
8
Neuroscientific Analysis of Logo Design: Implications for Luxury Brand Marketing.标志设计的神经科学分析:对奢侈品牌营销的启示
Behav Sci (Basel). 2025 Apr 9;15(4):502. doi: 10.3390/bs15040502.
9
The Double-Edged Sword of Anthropomorphism in LLMs .大语言模型中拟人化的双刃剑
Proceedings (MDPI). 2025 Feb 26;114(1):4. doi: 10.3390/proceedings2025114004.
10
On the emergent capabilities of ChatGPT 4 to estimate personality traits.关于ChatGPT 4评估人格特质的新兴能力。
Front Artif Intell. 2025 Feb 13;8:1484260. doi: 10.3389/frai.2025.1484260. eCollection 2025.
Am Psychol. 2023 Oct;78(7):901-917. doi: 10.1037/amp0001128. Epub 2023 Feb 27.
4
Using cognitive psychology to understand GPT-3.利用认知心理学理解 GPT-3。
Proc Natl Acad Sci U S A. 2023 Feb 7;120(6):e2218523120. doi: 10.1073/pnas.2218523120. Epub 2023 Feb 2.
5
Deep lexical hypothesis: Identifying personality structure in natural language.深度词汇假说:在自然语言中识别人格结构。
J Pers Soc Psychol. 2023 Jul;125(1):173-197. doi: 10.1037/pspp0000443. Epub 2022 Nov 17.
6
Realistic effort action plans (REAP) for exercise among underactive and inactive university students: A randomized trial.针对低活跃和不活跃大学生的现实努力行动计划(REAP):一项随机试验。
J Am Coll Health. 2024 Oct;72(7):2127-2136. doi: 10.1080/07448481.2022.2103382. Epub 2022 Aug 5.
7
Privacy in the age of psychological targeting.心理定位时代的隐私问题
Curr Opin Psychol. 2020 Feb;31:116-121. doi: 10.1016/j.copsyc.2019.08.010. Epub 2019 Aug 22.
8
Can Psychological Traits Be Inferred From Spending? Evidence From Transaction Data.消费能否揭示心理特征?来自交易数据的证据。
Psychol Sci. 2019 Jul;30(7):1087-1096. doi: 10.1177/0956797619849435. Epub 2019 Jun 5.
9
Morally Reframed Arguments Can Affect Support for Political Candidates.从道德层面重新构建的论点会影响对政治候选人的支持。
Soc Psychol Personal Sci. 2018 Nov;9(8):917-924. doi: 10.1177/1948550617729408. Epub 2017 Sep 28.
10
Psychological targeting as an effective approach to digital mass persuasion.心理定位作为一种有效的数字大众说服方法。
Proc Natl Acad Sci U S A. 2017 Nov 28;114(48):12714-12719. doi: 10.1073/pnas.1710966114. Epub 2017 Nov 13.