The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI
This work addresses the need for transferable tools to enhance trauma detection and intervention across diverse populations and settings, though it is incremental in applying existing methods to new data combinations.
The researchers tackled the problem of modeling traumatic event descriptions across diverse domains by training language models on multiple trauma-related datasets, achieving a fine-tuned RoBERTa model that slightly outperformed GPT-4 in cross-domain prediction and identified common traumatic events like sexual abuse and death-related experiences.
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.