Characterizing Online Eating Disorder Communities with Large Language
  Models
Minh Duc Chu Aryan Karnati Zihao He Kristina Lerman
The rise in eating disorders, a dangerous mental health condition with high
mortality and morbidity, has been linked to the proliferation of idealized body
images on social media. However, the link between social media and eating
disorders is far more complex. We argue that social media platforms create a
feedback loop that amplifies the growth of content and communities that promote
eating disorders like anorexia and bulimia. Specifically, social media
platforms make it easy for vulnerable individuals to find and connect to
like-minded others, while group dynamic processes encourage them to stay
engaged within communities that promote and glorify harmful behaviors linked to
eating disorders. We characterize this dynamic empirically through a
combination of network and language analysis. We describe a novel framework
that leverages large language models to analyze the discourse within online
communities and probe their attitudes on topics related to eating disorders to
identify potentially harmful content. Our work emphasizes the need for better
social media moderation to disrupt harmful feedback loops and protect
vulnerable individuals.