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 post-hoc analysis and evaluation of quality of service provision in digital mental health services.
Highlights
Experiences of mental health difficulties and emotional distress are increasing globally and, according to the World Health Organisation, 700,000 people die by suicide world-wide each year
To provide insights into the necessary components for learning mental health conversations, we examined the effects of changing: (i) the percentage of data used to pre-train the masked language model (MLM); (ii) the percentage of training data used to fine-tune the model for conversation stage classification; (iii) the use of text augmentation; and (iv) the inclusion of text from both the preceding and subsequent messages to aid classification
We have demonstrated the use of natural language processing (NLP) to gain insights into users and service provision of a digital mental health crisis service
Summary
Experiences of mental health difficulties and emotional distress are increasing globally and, according to the World Health Organisation, 700,000 people die by suicide world-wide each year. Traditional mental health services, such as talking therapies provided through the UK National Health Service, cannot meet current demand, nor can they reach people in distress in a discreet and accessible format. To provide aroundthe-clock support, digital mental health services are increasingly being developed as an accessible alternative, or as adjunct resources to traditional face-to-face services. While such services hold great promise, and various digital mental health apps and tools continue to enter the market, few resources are properly evaluated, holding back progress in developing high quality support [4, 5]
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