  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.
