Abstract
Social media is an important channel for communication, information dissemination, and social interaction, but also provides opportunities to illicitly sell goods online, including the trade of wildlife products. In this study, we use the Twitter public application programming interface (API) to access Twitter messages in order to detect and classify suspicious wildlife trafficking and sale using an unsupervised machine learning topic model combined with keyword filtering and manual annotation. We choose two prohibited wildlife animals and related products: elephant ivory and pangolin, and collected tweets containing keywords and known code words related to these species. In total, we collected 138,357 tweets filtered for these keywords over a 14-day period and were able to identify 53 tweets from 38 unique users that we suspect promoted the sale of Ivory products, though no pangolin related promoted post were detected in this study. Study results show that machine learning combined with supplement analysis approaches such as those utilized in this study have the potential to detect illegal content without the use of an existing training data set. If developed further, these approaches can help technology companies, conservation groups, and law enforcement officials to expedite the process of identifying illegal online sales and stem supply for the billion-dollar criminal industry of online wildlife trafficking.