Brief Research Report ARTICLE
Use of Machine Learning to Detect Illegal Wildlife Product Promotion and Sales on Twitter
- 1Extension - Department of Healthcare Research and Policy, University of California, San Diego, United States
- 2Global Health Policy Institute, United States
- 3Department of Computer Science and Engineering, University of California, San Diego, United States
- 4Department of Anesthesiology and Division of Infectious Diseases and Global Public Health, University of California, San Diego, United States
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 fourteen-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.
Keywords: wildlife conservation, Social Media, Machine Learing, Wildlife trafficking, Twitter
Received: 15 May 2019;
Accepted: 30 Jul 2019.
Copyright: © 2019 Xu, Li, Cai and Mackey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Tim K. Mackey, University of California, San Diego, Department of Anesthesiology and Division of Infectious Diseases and Global Public Health, San Diego, United States, firstname.lastname@example.org