Evaluating the Influence of Role-Playing Prompts on ChatGPT's Misinformation Detection Accuracy: Quantitative Study.

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES JMIR infodemiology Pub Date : 2024-09-26 DOI:10.2196/60678
Michael Robert Haupt, Luning Yang, Tina Purnat, Tim Mackey
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Abstract

Background: During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI's ChatGPT imitates specific social roles or identities. This research examines how ChatGPT's accuracy in detecting COVID-19-related misinformation is affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cues can inform messaging campaigns, ensuring effective use in public health communications.

Objective: This study investigates the impact of role-playing prompts on ChatGPT's accuracy in detecting misinformation. This study also assesses differences in performance when misinformation is explicitly stated versus implied, based on contextual knowledge, and examines the reasoning given by ChatGPT for classification decisions.

Methods: Overall, 36 real-world tweets about COVID-19 collected in September 2021 were categorized into misinformation, sentiment (opinions aligned vs unaligned with public health guidelines), corrections, and neutral reporting. ChatGPT was tested with prompts incorporating different combinations of multiple social identities (ie, political beliefs, education levels, locality, religiosity, and personality traits), resulting in 51,840 runs. Two control conditions were used to compare results: prompts with no identities and those including only political identity.

Results: The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable drop from 68.1% (SD 41.2%; no identities) to 29.3% (SD 31.6%; all identities included). Prompts with only political identity resulted in the lowest accuracy (19.2%, SD 29.2%). ChatGPT was also able to distinguish between sentiments expressing opinions not aligned with public health guidelines from misinformation making declarative statements. There were no consistent differences in performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that the inclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with social identities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it did when assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT's explanations for its classification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanations were provided in some instances.

Conclusions: These results indicate that ChatGPT's ability to classify misinformation is negatively impacted when role-playing social identities, highlighting the complexity of integrating human biases and perspectives in LLMs. This points to the need for human oversight in the use of LLMs for misinformation detection. Further research is needed to understand how LLMs weigh social identities in prompt-based tasks and explore their application in different cultural contexts.

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评估角色扮演提示对 ChatGPT 错误信息检测准确性的影响:定量研究。
背景:在 COVID-19 大流行期间,社交媒体上错误信息的快速传播给公共卫生带来了巨大挑战。在大量文本数据上进行预训练的大型语言模型(LLM)在检测错误信息方面已显示出潜力,但其性能可能会受到提示工程(即修改 LLM 请求以评估输出变化)等因素的影响。角色扮演是提示工程的一种形式,OpenAI 的 ChatGPT 会根据请求模仿特定的社会角色或身份。本研究探讨了当 ChatGPT 在请求提示中被赋予社会身份时,其检测 COVID-19 相关错误信息的准确性会受到怎样的影响。了解 LLM 对不同身份提示的反应可以为信息传播活动提供参考,确保在公共健康传播中的有效使用:本研究调查了角色扮演提示对 ChatGPT 检测错误信息准确性的影响。本研究还根据上下文知识,评估了明示与暗示错误信息时的性能差异,并考察了 ChatGPT 在做出分类决定时给出的推理:总体而言,2021 年 9 月收集的有关 COVID-19 的 36 条真实推文被分为错误信息、情绪(与公共卫生指南一致与不一致的观点)、更正和中立报告。ChatGPT 测试了多种社会身份(即政治信仰、教育水平、地域、宗教信仰和个性特征)的不同组合提示,共运行了 51840 次。比较结果时使用了两种对照条件:不包含身份的提示和只包含政治身份的提示:结果显示,在提示中包含社会身份会降低平均检测准确率,从 68.1%(标准差 41.2%;无身份)显著降至 29.3%(标准差 31.6%;包含所有身份)。只有政治身份的提示准确率最低(19.2%,标准差 29.2%)。ChatGPT 还能区分表达不符合公共卫生准则的观点的情绪和发表宣言的错误信息。在需要上下文知识的显性和隐性错误信息之间,表现没有一致的差异。虽然研究结果表明加入身份会降低检测准确率,但仍不能确定 ChatGPT 是否采纳了与社会身份相一致的观点:当被赋予保守身份时,ChatGPT 识别错误信息的准确率与被赋予自由身份时几乎相同。虽然 ChatGPT 在解释其分类决定时最常提到的是政治身份,但在不同的研究条件下,分类的理由并不一致,而且在某些情况下还提供了相互矛盾的解释:这些结果表明,在角色扮演社会身份时,ChatGPT 对错误信息进行分类的能力会受到负面影响,这凸显了在 LLM 中整合人类偏见和观点的复杂性。这说明在使用 LLMs 检测错误信息时需要人为监督。要了解 LLMs 在基于提示的任务中如何权衡社会身份,并探索其在不同文化背景下的应用,还需要进一步的研究。
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Correction: Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences. The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: A Call for Comprehensive Research on Sleep Infodemiology and Infoveillance. Understanding and Combating Misinformation: An Evolutionary Perspective. Detection and Characterization of Online Substance Use Discussions Among Gamers: Qualitative Retrospective Analysis of Reddit r/StopGaming Data. Evaluating the Influence of Role-Playing Prompts on ChatGPT's Misinformation Detection Accuracy: Quantitative Study.
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