Eating disorders (ED) are pervasive and do not discriminate based on race, religion, gender, or SES. Comorbidities include anxiety, depression, substance abuse, self-injurious behaviors, and history of trauma. ED are often a lifelong struggle with approximately 2⁄3 of patients never achieving a full and sustained remission. ED are the product, in part, of increased societal pressures to fit “the thin ideal”. These pressures come in the form of repeated advertisements on various media platforms, messages from the diet and exercise industries, fashion industry “norms”, etc. Individuals who suffer from ED may have experienced trauma and/or have difficult home lives. The ED can provide a sense of control over these factors, albeit an invalid one. Exposure to media expressing “the thin ideal” can be triggering to individuals with ED as well as those at risk for developing them. Social media platforms are especially rife with these triggers. Concurrent with the rise of social media, individuals with ED have created communities1 in which they support one another in the dangerous pursuit of this illness’ elusive goal: to be “thin enough”. Websites promoting anorexia (pro-ana) and bulimia (pro-mia) as lifestyle choices valorize acting on ED symptoms. Such sites teach those suffering or at risk from ED how to develop, act on, and hide the illness, and support them in doing so, putting them at risk for serious physical and mental health complications, including death. The impact of images in this community far exceeds that of other communities surrounding physical and mental health issues. Therefore, it is important that clinicians and family members be able to identify websites containing images that are associated with promotion of anorexia and bulimia in order to prevent accidental or intentional exposure to these triggers. This research aims to automatically identify such triggering material, with the ultimate goal of designing parental and clinical controls. We report on a proof of concept, machine learning approach to identify pro-ana content, trained on example data from online social media searches. The training data is chosen to compare pro-ana content with other content similar in demographics and photographic style.