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Characterisation of mental health conditions in social media using Informed Deep Learning
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-4178-2980
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
The Author(s) SN - , 2017. Vol. 7
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Language Technology (Computational Linguistics)
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URN: urn:nbn:se:kth:diva-204776DOI: 10.1038/srep45141ISI: 000396994200001Scopus ID: 2-s2.0-85016019626OAI: oai:DiVA.org:kth-204776DiVA: diva2:1086111
Note

QC 20170418

Available from: 2017-03-31 Created: 2017-03-31 Last updated: 2017-04-18Bibliographically approved

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Publisher's full textScopushttp://dx.doi.org/10.1038/srep45141

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CiteExportLink to record
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Citation style
  • apa
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