Towards Safer Online Spaces: Simulating and Assessing Intervention Strategies for Eating Disorder Discussions

TL;DR

This paper introduces a novel LLM-based testbed for simulating and evaluating intervention strategies in online discussions about eating disorders, revealing insights into their effectiveness and biases across different models and platforms.

Contribution

It presents a new framework for synthetic conversation generation and analysis, enabling safe testing of intervention strategies in ED-related social media discussions.

Findings

Civility-focused interventions improve sentiment and emotional tone.

Insight-resetting approaches increase negative emotions.

Significant biases exist across different LLMs and versions.

Abstract

Eating disorders are complex mental health conditions that affect millions of people around the world. Effective interventions on social media platforms are crucial, yet testing strategies in situ can be risky. We present a novel LLM-driven experimental testbed for simulating and assessing intervention strategies in ED-related discussions. Our framework generates synthetic conversations across multiple platforms, models, and ED-related topics, allowing for controlled experimentation with diverse intervention approaches. We analyze the impact of various intervention strategies on conversation dynamics across four dimensions: intervention type, generative model, social media platform, and ED-related community/topic. We employ cognitive domain analysis metrics, including sentiment, emotions, etc., to evaluate the effectiveness of interventions. Our findings reveal that civility-focused interventions consistently improve positive sentiment and emotional tone across all dimensions, while insight-resetting approaches tend to increase negative emotions. We also uncover significant biases in LLM-generated conversations, with cognitive metrics varying notably between models (Claude-3 Haiku $>$ Mistral $>$ GPT-3.5-turbo $>$ LLaMA3) and even between versions of the same model. These variations highlight the importance of model selection in simulating realistic discussions related to ED. Our work provides valuable information on the complex dynamics of ED-related discussions and the effectiveness of various intervention strategies.