We compiled a dataset consisting of all the species involved in the illegal wildlife trade along with the reason (i.e., use-type) they were being traded. In total, the dataset includes c. 4.9k distinct taxa representing c. 3.3k species and contains c. 11k taxa-use combinations from 110 unique use-types. Our dataset can be used to conduct large-scale broad searches of the Internet to find illegally traded wildlife.
The Internet can be vast source of data for the wildlife trade. However, data collected from the Internet is often numerous and messy, making data cleaning a task the requires a lot time and effort. Here, we tested if text classification can be used to speed up the process of data cleaning in relation to online data collected on the wildlife trade. We found that text classification models can predict with great accuracy relaxant advertisements, including the taxonomy of relevant species, using the text found in online advertisements. We recommend using text classification as a method to make data cleaning more efficient. Future efforts should try to pair text classification with image classification for improved efficiency.
The internet is a vast source of wildlife trade data. Here, we present an accessible guide for Internet‐based wildlife trade surveillance, which uses a repeatable and systematic method to automate data collection from relevant websites. Our guide is adaptable to the multitude of trade‐based contexts including different focal taxa or derived parts, and locations of interest.