The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI (2024)

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 various 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.

## introduction post-traumatic stress disorder (ptsd) is a significant mental health condition that can develop after experiencing a traumatic event. for an event to potentially lead to ptsd, it must involve actual or threatened death, serious injury, or a threat to one's physical integrity, causing intense fear, helplessness, or horror @xcite . although about 70% of americans will encounter such traumatic events in their lifetime, only about 5-7% develop ptsd, highlighting that ptsd is relatively rare despite high trauma exposure. this figure could be even higher, as many cases may go undiagnosed @xcite . this discrepancy suggests that various factors, including psychological resilience, the nature of the trauma, and access to mental health support, influence the development of ptsd. definitions of trauma and responses to it can vary widely across cultures and social contexts, affecting the prevalence and expression of ptsd. to investigate the interplay of these factors, we are proposing a natural language processing (nlp) approach to identify traumatic events across different domains. understanding the crossdomain mechanisms of trauma is crucial for developing comprehensive support systems and interventions that are adaptable to various contexts. to address this, we are following up on these research questions: rq1: given the diverse forms of trauma, what are the most effective methods for modeling and predicting its manifestations? rq2: how transferable is the detection of multifaceted traumatic events across domains? rq3: what are the cross-cutting mechanisms related to trauma that can be identified across different types and contexts of traumatic events? our work advances trauma detection by applying nlp and explainable ai (xai) methods to offer detailed insights not yet explored in the literature. we contribute by: ## model performance results classification performance we fit all the models to the respective datasets after performing hyperparameter optimization (cf. appendix a.2) and report their performance metrics in cross-domain performance figure 2 presents the cross-domain results of roberta models fine-tuned on one dataset and evaluated on other datasets, using the auc-roc metric (cf., appendix a, the results show that the roberta model finetuned on the ptsd dataset has the best generalizability across different datasets, with models trained on the full data also performing well. given the diversity of traumatic events across datasets, this result suggests the trauma features in the ptsd dataset are broadly applicable for learning a general event type rather than causing models to pick up on only keywords. counseling-trained models perform well on their own dataset but do not generalize as effectively. performance on the incel dataset indicates all models effectively differentiate trauma-related vocabulary from control data. ## conclusion traumatic events shape millions of lives. computational tools to recognize these events can help third parties provide support. however, their diversity makes classification challenging. this paper introduces a new dataset for recognizing traumatic events and analyzes (i) nlp models' performance, (ii) their generalizability across domains, and (iii) whether they learn general trauma features using xai techniques. we show that transformerbased models, particularly fine-tuned bert and roberta, outperform traditional models and neural networks, although simpler models achieve very close scores. models trained on diverse datasets, like the combined dataset model, perform well across various contexts, emphasizing the importance of exposure to varied data. despite unique aspects in datasets like gtc's military component, shared trauma themes enable strong cross-domain generalizability. notably, large language models like gpt-4 do not necessarily improve performance on this task without fine-tuning and domainspecific adjustments. our analysis shows that while certain features of trauma are context-specific, there are also universal elements across different experiences. however, certain types of traumatic events-notably mental abuse-are particularly challenging to classify due to their less defined nature and greater variability, highlighting the need for clear definitions and enhanced model performance.