# Large Language Models Help Reveal Unhealthy Diet and Body Concerns in Online Eating Disorders Communities

arXiv: https://arxiv.org/abs/2401.09647 · HTML: https://mlsanity.com/paper/2401.09647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Evaluating and Inducing Personality in Pre-trained Language Models](https://mlsanity.com/paper/2206.07550/) (2022)
- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key](https://mlsanity.com/paper/2104.10788/) (2021)
- [Large Language Models as Subpopulation Representative Models: A Review](https://mlsanity.com/paper/2310.17888/) (2023)
- [Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora](https://mlsanity.com/paper/2211.16259/) (2022)
- [Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter](https://mlsanity.com/paper/2209.09339/) (2022)
- [Quantifying How Hateful Communities Radicalize Online Users](https://mlsanity.com/paper/2209.08697/) (2022)

## Built on by (most cited first, 1 of 1)

- [Early Linguistic Pattern of Anxiety from Social Media Using Interpretable Linguistic Features: A Multi-Faceted Validation Study with Author-Disjoint Evaluation](https://mlsanity.com/paper/2601.11758/) (2026)

## Abstract (arXiv)

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.