In their efforts to call attention to environmental problems, communicate with like-minded groups, and mobilize support for their activities, radical environmentalist organizations produce an enormous amount of text. These texts, like radical environmental groups themselves, are often (i) densely connected and (ii) highly variable in advocated protest activities. Given a corpus of radical environmentalist texts, can one uncover the underlying network structure of environmental (and related leftist) groups? If so, can one then also identify which groups (and which sub-networks) are more prone to violent versus nonviolent 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 and more modern statistical 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.