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. 2017 Mar 22:7:45141.
doi: 10.1038/srep45141.

Characterisation of mental health conditions in social media using Informed Deep Learning

George Gkotsis  1 , Anika Oellrich  1 , Sumithra Velupillai  1   2 , Maria Liakata  3 , Tim J P Hubbard  4 , Richard J B Dobson  1   5 , Rina Dutta  1
Affiliations

Characterisation of mental health conditions in social media using Informed Deep Learning

George Gkotsis et al. Sci Rep. .

Erratum in

Abstract

The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of 'in the moment' daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Overall workflow of our approach.
Figure 2
Figure 2. Multiclass classification confusion matrix using a Convolutional Neural Network (CNN) classifier.
Figure 3
Figure 3
Architecture for the Feed Forward (a) and Convolutional Neural Network (b) deep learning approaches.
See this image and copyright information in PMC

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