Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health.
However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing NLP applications within this setting. Around , messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and bayesian optimization for hyperparameter search.
A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines. Mental disorders are highly prevalent 1. Being responsible for around a third of all years lived in disability 2 makes them a major public health concern.
While occurring across the lifespan, early life phases appear to be particularly critical for mental health, as a majority of disorders develop within the first 24 years of life 3 , and symptoms are likely to sustain into later life phases 4.
However, most affected young people are not accessing care 5 , 6 , commonly due to a lack of knowledge, perceived stigma and structural barriers 7.
