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Social media platforms such as Instagram and Twitter have emerged as criticalchannels for drug marketing and illegal sale. Detecting and labeling onlineillicit drug trafficking activities becomes important in addressing this issue.However, the effectiveness of conventional supervised learning methods indetecting drug trafficking heavily relies on having access to substantialamounts of labeled data, while data annotation is time-consuming andresource-intensive. Furthermore, these models often face challenges inaccurately identifying trafficking activities when drug dealers use deceptivelanguage and euphemisms to avoid detection. To overcome this limitation, weconduct 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-informedprompts}, which serve as the interface that humans can interact with and useLLMs to perform the detection task. Additionally, we design a Monte Carlodropout based prompt optimization method to further to improve performance andinterpretability. Our experimental findings demonstrate that the proposedframework outperforms other baseline language models in terms of drugtrafficking detection accuracy, showing a remarkable improvement of nearly12\%. By integrating prior knowledge and the proposed prompts, ChatGPT caneffectively identify and label drug trafficking activities on social networks,even in the presence of deceptive language and euphemisms used by drug dealersto evade detection. The implications of our research extend to social networks,emphasizing the importance of incorporating prior knowledge and scenario-basedprompts into analytical tools to improve online security and public safety.

Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media