Knowledge-prompted ChatGPT: Enhancing drug trafficking detection on social media

Abstract

Social media platforms such as Instagram and Twitter have emerged as critical channels for marketing and selling illegal drugs. Detecting and labeling online illicit drug trafficking activities have become an important measure to combat online drug trafficking. Recently, machine learning has been applied to drug trafficking detection. However, the effectiveness of conventional supervised learning methods in detecting drug trafficking heavily relies on 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 knowledge-informed prompts, which serve as the interface that humans can interact with and use LLMs to perform the detection task. Additionally, we designed a Monte Carlo dropout-based prompt optimization method to further 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.