Eating disorders (EDs) constitute a widespread group of mental illnesses affecting the everyday life of many individuals in all age groups. One of the main difficulties in the diagnosis and treatment of these disorders is the interpersonal variability of symptoms and the variety of underlying psychological states that are not considered in traditional approaches. In order to gain a better understanding of these disorders, many studies have collected data from social media and analysed them from a computational perspective, but the resulting dataset were very limited and task-specific. Aiming to address this shortage by providing a dataset that could be easily adapted to different tasks, we built a corpus collecting ED-related and ED-unrelated comments from Reddit focusing on a limited number of topics (fitness, nutrition, etc.). To validate the effectiveness of the dataset, we evaluated the performance of two classifiers in distinguishing between ED-related and unrelated comments. The high-level accuracy of both classifiers indicates that ED-related texts are separable from texts on similar topics that do not address EDs. For explorative purposes, we also carried out a linguistic analysis of word class dominance in ED-related texts, whose results are consistent with the findings of psychological research on EDs.