  Eating disorders (ED), a severe mental health condition with high rates of
mortality and morbidity, affect millions of people globally, especially
adolescents. The proliferation of online communities that promote and normalize
ED has been linked to this public health crisis. However, identifying harmful
communities is challenging due to the use of coded language and other
obfuscations. To address this challenge, we propose a novel framework to
surface implicit attitudes of online communities by adapting large language
models (LLMs) to the language of the community. We describe an alignment method
and evaluate results along multiple dimensions of semantics and affect. We then
use the community-aligned LLM to respond to psychometric questionnaires
designed to identify ED in individuals. We demonstrate that LLMs can
effectively adopt community-specific perspectives and reveal significant
variations in eating disorder risks in different online communities. These
findings highlight the utility of LLMs to reveal implicit attitudes and
collective mindsets of communities, offering new tools for mitigating harmful
content on social media.
