Self-Harm Detection for Mental Health Chatbots.

TLDR: In this paper, a self-harm classifier was designed to predict whether a user's response to a chatbot indicates intent for selfharm, based on text input from the user.

Abstract: Chatbots potentially address deficits in availability of the traditional health workforce and could help to stem concerning rates of youth mental health issues including high suicide rates. While chatbots have shown some positive results in helping people cope with mental health issues, there are yet deep concerns regarding such chatbots in terms of their ability to identify emergency situations and act accordingly. Risk of suicide/self-harm is one such concern which we have addressed in this project. A chatbot decides its response based on the text input from the user and must correctly recognize the significance of a given input. We have designed a self-harm classifier which could use the user's response to the chatbot and predict whether the response indicates intent for self-harm. With the difficulty to access confidential counselling data, we looked for alternate data sources and found Twitter and Reddit to provide data similar to what we would expect to get from a chatbot user. We trained a sentiment analysis classifier on Twitter data and a self-harm classifier on the Reddit data. We combined the results of the two models to improve the model performance. We got the best results from a LSTM-RNN classifier using BERT encoding. The best model accuracy achieved was 92.13%. We tested the model on new data from Reddit and got an impressive result with an accuracy of 97%. Such a model is promising for future embedding in mental health chatbots to improve their safety through accurate detection of self-harm talk by users.