Radical social movements are broadly engaged in, and dedicated to, promoting change in their social environment. In their corresponding efforts to call attention to various causes, communicate with like-minded groups, and mobilize support for their activities, radical social movements also produce an enormous amount of text. These texts, like radical social movements themselves, are often (i) densely connected and (ii) highly variable in advocated protest activities. Given a corpus of radical social movement texts, can one uncover the underlying network structure of the radical activist groups involved in this movement? If so, can one then also identify which groups (and which subnetworks) are more prone to radical versus mainstream protest activities? Using a large corpus of British radical environmentalist texts (1992-2003), we seek to answer these questions through a novel integration of network discovery and unsupervised topic modeling. In doing so, we apply classic network descriptives (e.g., centrality measures) and more modern statistical models (e.g., exponential random graph models) to carefully parse apart these questions. Our findings provide a number of revealing insights into the networks and nature of radical environmentalists and their texts.