Improving mental health using machine learning to assist humans in the moderation of forum posts

Wang, Dong, Weeds, Julie and Comley, Ian (2020) Improving mental health using machine learning to assist humans in the moderation of forum posts. Health Informatics, Valletta, Malta, 24-26th February 2020. Published in: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies. 5 187-197. Science and Technology Publications ISSN 2184-4305 ISBN 9789897583988

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Abstract

This work investigates the potential for the application of machine learning and natural language processing technology in an online application designed to help teenagers talk about their mental health issues. Specifically, we investigate whether automatic classification methods can be applied with sufficient accuracy to assist humans in the moderation of posts and replies to an online forum. Using real data from an existing application, we outline the specific problems of lack of data, class imbalance and multiple rejection reasons. We investigate a number of machine learning architectures including a state-of-the-art transfer learning architecture, BERT, which has performed well elsewhere despite limited training data, due to its use of pre-training on a very large general corpus. Evaluating on real data, we demonstrate that further large performance gains can be made through the use of automatic data augmentation techniques (synonym replacement, synonym insertion, random swap and random deletion). Using a combination of data augmentation and transfer learning, performance of the automatic classification rivals human performance at the task, thus demonstrating the feasibility of deploying these techniques in a live system.

Item Type: Conference Proceedings
Keywords: Machine Learning, Natural Language Processing, Mental Health, Online Forum Moderation, Data Augmentation, BERT, LSTM.
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Data Science Research Group
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User: Julie Weeds
Date Deposited: 06 Feb 2020 09:04
Last Modified: 07 Apr 2020 09:40
URI: http://sro.sussex.ac.uk/id/eprint/89769

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