揭示知识提示型ChatGPT在增强社交媒体毒品贩运检测中的潜力
Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media
AI总结：
本文首次系统研究利用ChatGPT等大语言模型检测社交媒体毒品贩运，提出知识知情提示框架和蒙特卡洛dropout提示优化方法，检测准确率较基线模型提升近12%，有效应对毒贩的欺骗性语言和委婉语。
摘要：
Instagram和Twitter等社交媒体平台已成为毒品营销和非法销售的关键渠道。检测并标注在线非法毒品贩运活动对于解决这一问题变得十分重要。然而，传统监督学习方法在检测毒品贩运方面的有效性在很大程度上依赖于获取大量标注数据，而数据标注耗时且资源密集。此外，当毒贩使用欺骗性语言和委婉语来逃避检测时，这些模型在准确识别贩运活动方面往往面临挑战。为克服这一局限，我们开展了首个系统性研究，利用ChatGPT等大语言模型（LLMs）来检测社交媒体上的非法毒品贩运活动。我们提出了一个分析框架来构建知识知情提示，这些提示作为人类可以与LLMs交互并利用其执行检测任务的接口。此外，我们设计了一种基于蒙特卡洛dropout的提示优化方法，以进一步提高性能和可解释性。我们的实验结果表明，所提出的框架在毒品贩运检测准确率方面优于其他基线语言模型，显示出接近12%的显著提升。通过整合先验知识和所提出的提示，ChatGPT能够有效识别和标注社交网络上的毒品贩运活动，即使在毒贩为逃避检测而使用欺骗性语言和委婉语的情况下也是如此。我们的研究意义延伸到社交网络领域，强调了将先验知识和基于场景的提示纳入分析工具以提高在线安全和公共安全的重要性。
英文摘要：
Social media platforms such as Instagram and Twitter have emerged as critical channels for drug marketing and illegal sale. Detecting and labeling online illicit drug trafficking activities becomes important in addressing this issue. However, the effectiveness of conventional supervised learning methods in detecting drug trafficking heavily relies on having access to substantial amounts of labeled data, while data annotation is time-consuming and resource-intensive. Furthermore, these models often face challenges in accurately identifying trafficking activities when drug dealers use deceptive language and euphemisms to avoid detection. To overcome this limitation, we conduct the first systematic study on leveraging large language models (LLMs), such as ChatGPT, to detect illicit drug trafficking activities on social media. We propose an analytical framework to compose \emph{knowledge-informed prompts}, which serve as the interface that humans can interact with and use LLMs to perform the detection task. Additionally, we design a Monte Carlo dropout based prompt optimization method to further to improve performance and interpretability. Our experimental findings demonstrate that the proposed framework outperforms other baseline language models in terms of drug trafficking detection accuracy, showing a remarkable improvement of nearly 12\%. By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection. The implications of our research extend to social networks, emphasizing the importance of incorporating prior knowledge and scenario-based prompts into analytical tools to improve online security and public safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
