About

Overview

Eating disorders (ED) are serious medical conditions that can have disastrous effects on an individual’s mental and physical health. They are pervasive, and do not discriminate based on race, religion, gender, or socioeconomic status. ED are often a lifelong struggle, with approximately ⅔ of patients never achieving a full and sustained remission. They are the product, in part, of increased societal pressures to fit “the thin ideal,” and exposure to this media can be triggering to people with ED as well as those at risk for developing them. Social media platforms are especially rife with these triggers—individuals with ED have created communities where they support one another in the dangerous pursuit of being “thin enough.” These websites teach readers how to act on and hide their ED, putting them at risk for severe physical and mental health complications, including death. Therefore, triggering content online poses a serious risk to social media users with ED and those at risk for developing them. Similarly, it is essential that clinicians and family members be able to identify websites containing images that are associated with the promotion of ED to prevent accidental or intentional exposure to these triggers. However, it is challenging for caretakers to find and stay up-to-date with ED communities and content online.

This project aims to automatically detect such triggering material, with the ultimate goal of designing tools to inform clinicians and support patients in their recovery. The main products of this work are a convolutional neural network that identifies images of ED and two novel software tools built from it that assess websites for ED content. These tools would enable clinicians, family members, and those suffering from ED to understand and identify sources of ED content to improve treatment for ED patients.

Samsara Counts

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