LAUSR

Summary At an estimated US$19 billion, the illicit wildlife trade is a serious threat to global conservation efforts. This criminal enterprise is now digital, expanding its footprint to consumers internationally by using the Internet and social media platforms. Recent studies have detected illegal wildlife selling posts on the popular social networking site Facebook in several different languages, including Chinese. In order to further explore this challenge to conservation, this study used big data approaches to identify and characterize wildlife trading activity in Chinese language on Facebook using an automated web scraper. We focused on keywords associated with elephants, rhinos and hawksbill turtles. We collected 10 303 unique Facebook posts over a 45-day period and were able to identify 639 posts from 268 unique users, which we suspect of directly marketing the sale of wildlife products. We also identified other species including Tibetan antelope, bears and African spurred tortoises. Facebook community pages appeared to have the highest percentage (48.2%) of wildlife selling posts. We also identified 14 different countries and regions with suspected wildlife-selling users, most located in Taiwan. Furthermore, we observed that the language used by some sellers changed from descriptive text to emojis and other code words. Collective action is needed from governments, law enforcement, civil society and technology companies leveraging big data approaches to better detect and interdict online Chinese-language wildlife trafficking.