The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI
<|reference_start|>The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI: 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.<|reference_end|>
@article{schirmer2024the,
    title={The Language of Trauma: Modeling Traumatic Event Descriptions Across
  Domains with Explainable AI},
    author={Miriam Schirmer, Tobias Leemann, Gjergji Kasneci, J"urgen Pfeffer,
  David Jurgens},
    journal={arXiv preprint arXiv:2408.05977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05977},
    primaryClass={cs.CL cs.CY}
}