To the Editor — Illegal trade in wildlife is booming on e-commerce platforms1, the ‘dark web’2 and social media1,3. The ease of access and high number of users of social media make it a particularly concerning venue1,3,4. Wildlife dealers use social media to release photos and information about products to attract customers and to market their products to networks of contacts. We currently lack the tools for automated monitoring of high-volume data that are needed to investigate and prevent this illegal trade, but machine-learning algorithms offer a way forward. Operating within the broader field of artificial intelligence, the concept of machine learning refers to algorithms that learn from data without human guidance. Deep-learning algorithms5 are a family of these algorithms that are highly successful in classifying image contents and locating individual objects within them, and in processing natural language. Applying these techniques to social media data allows human behaviour to be investigated on an unprecedented scale. Yet these techniques and data sources are still rarely used to address drivers of the biodiversity crisis6. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Processing such data manually is inefficient and time consuming, but machine-learning algorithms can be trained to filter this content to identify relevant information (see Fig. 1 for an example). These algorithms can be trained to detect which species or wildlife products, such as horns or scales, appear in an image or video, while also classifying their setting, such as a natural habitat or a marketplace. When processing video, algorithms can use audio clues, such as identifying bird species by their songs and calls, as well as interrogating the image stream. Natural language processing can be used to infer the meaning of a verbal description, for example, whether an animal or plant is for sale or observed in nature, and to classify the sentiment and preferences of social media users. To unlock this potential, social media platforms must be engaged to share their data and actively collaborate in the development of real-time monitoring tools that can be used to automatically identify content pertaining to the illegal wildlife trade, and to report this content to enforcers. Furthermore, machine-learning algorithms need humanverified training data. Such training datasets may be generated through crowd-sourcing initiatives, and collaborations between scientists and enforcers may further improve the algorithms’ performance. Together with advances in artificial intelligence that will refine the algorithms themselves, such efforts