Wildlife Product Trading in Online Social Networks: A Case Study on Ivory-Related Product Sales Promotion Posts

Abstract

Wildlife trafficking (WLT) has evolved into a pressing global concern, as traffickers increasingly utilize online platforms such as e-commerce websites and social networks to expand their illicit trade. This paper addresses the pivotal challenge of detecting and recognizing promotional behaviors related to the sale of wildlife products within online social networks—a critical step in combating these environmentally detrimental activities. To confront these illicit operations effectively, our research undertakes the following key initiatives: 1. Data Collection and Labeling: We employ a network-based approach to gather a scalable dataset pertaining to wildlife product trading. Through a human-in-the-loop machine learning process, this dataset is meticulously labeled, distinguishing between positive class samples containing wildlife product selling posts and hard-negatives representing regular posts misclassified as potential WLT posts, subsequently rectified by human annotators. 2. Machine Learning Framework Development: We present a robust framework that benchmarks machine learning results on the collected dataset. This framework autonomously identifies suspicious wildlife selling posts and accounts, effectively harnessing the multi-modal nature of online social networks. 3. In-depth Analysis of Trading Behaviors: Our research delves into a comprehensive analysis of trading posts, illuminating the systematic and organized selling behaviors prevalent in the current landscape. By providing detailed insights into the nature of these behaviors, we contribute valuable information for understanding and countering illegal wildlife product trading. Moreover, we emphasize our commitment to openness and collaboration by making our code and dataset openly available, thereby fostering cooperative efforts towards the development of more effective strategies in combating illegal wildlife trafficking.