The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI

TL;DR

This study trains and evaluates language models on diverse trauma-related datasets, demonstrating transferability of trauma detection across domains and highlighting key trauma indicators using explainable AI techniques.

Contribution

It introduces a multi-domain trauma language modeling approach with explainability, outperforming some large language models in cross-domain trauma prediction.

Findings

RoBERTa outperforms GPT-4 in trauma prediction

SLALOM scores effectively differentiate trauma aspects

Sexual abuse and death-related experiences are common trauma indicators

Abstract

Psychological trauma can manifest following various distressing events and is captured in diverse online contexts. However, studies traditionally focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. We address this gap by training language models with progressing complexity on trauma-related datasets, including genocide-related court data, a Reddit dataset on post-traumatic stress disorder (PTSD), counseling conversations, and Incel forum posts. Our results show that the fine-tuned RoBERTa model excels in predicting traumatic events across domains, slightly outperforming large language models like GPT-4. Additionally, SLALOM-feature scores and conceptual explanations effectively differentiate and cluster trauma-related language, highlighting different trauma aspects and identifying sexual abuse and experiences related to death as a common traumatic event across all datasets. This transferability is crucial as it allows for the development of tools to enhance trauma detection and intervention in diverse populations and settings.