ArticleFrontiers in digital health2021
Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service.
Article in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Current applications and future directions in natural language processing for news media and mental health.Scientific reports · 2025Article
- Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study.JMIR AI · 2025Article
- Personalized insights into liver disease management: a text mining analysis of online consultation data.Frontiers in public health · 2025Article
- Adolescent help-seeking on a psychological support hotline: latent classes, predictors, and distal outcomes.Frontiers in psychiatry · 2025Article
- Using hidden Markov modelling to reveal in-session stages in text-based counselling.Npj mental health research · 2024Article
- The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review.Cureus · 2024Review
- Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA.Scientific reports · 2023Article
- Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study.Journal of medical Internet research · 2022Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.