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Data from digital media are increasingly used in conservation science. They help researchers and policymakers learn about the beliefs and values of people and their interactions with non-human nature, and observe relevant news and events on a global scale. In the ongoing global biodiversity crisis, this knowledge becomes crucial. It is predominantly human activities that drive plant and animal species into extinction and destroy sensitive ecosystems (on which our wellbeing and survival depends). Knowing about people’s environmental perceptions is an important stepping stone in promoting a change away from unsustainable habits and a transition to more sustainable lifestyles. In this thesis, I explore, develop, and evaluate novel methods and approaches to unlock the new opportunities digital media offer to conservation science. The five individual publications that form chapters of the thesis build upon each other: first, I present a comprehensive review of relevant data sources and methods. In the second chapter, I discuss the data privacy implications of using digital media data in conservation science research. The third chapter presents a general framework to collect, filter, and identify social media data related to illegal wildlife trade. The fourth and fifth chapters, then, combine the findings of the first three: I demonstrate how to identify conservation-relevant events from the public opinion on social media and in online news, and investigate the online songbird trade in Indonesia, its price structure and the spatial characteristics of its supply chain. The concepts, the methods, and the openly-available tools presented in this thesis can guide …