Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language. (arXiv:2209.04830v1 [cs.CL])

With the increased awareness of situations of mental crisis and their
societal impact, online services providing emergency support are becoming
commonplace in many countries. Computational models, trained on discussions
between help-seekers and providers, can support suicide prevention by
identifying at-risk individuals. However, the lack of domain-specific models,
especially in low-resource languages, poses a significant challenge for the
automatic detection of suicide risk. We propose a model that combines
pre-trained language models (PLM) with a fixed set of manually crafted (and …

arxiv language risk services study