Characterisation of mental health conditions in social media using Informed Deep Learning
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 536
- Type
- D - Journal article
- DOI
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10.1038/srep45141
- Title of journal
- Scientific Reports
- Article number
- ARTN 45141
- First page
- 45141
- Volume
- 7
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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6
- Research group(s)
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-
- Citation count
- 36
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Widely cited in health & medical informatics, social media & mental health, behavioural health, psychiatry, suicidality. Venues citing this paper include CIKM 2018 (core A), IEEE, SAGE journals, Elsevier journals, Nature journals, Journal of Medical Internet Research (JMIR). Resulted in a subsequent paper in the Journal of Biomedical Semantics (https://www.sciencedirect.com/science/article/pii/S1532046418302016 ) and MRC funding with KCL collaborators (https://kclpure.kcl.ac.uk/portal/en/projects/social-media-smartphone-use-and-selfharm-in-young-people-3syp-study(ebf82306-37c5-4d00-a2f3-b56046e10916).html) as well as interviews by the media: http://www.bbc.com/future/story/20190207-how-artificial-intelligence-can-help-stop-bullying. Featured in BBC Brazil and Entorno Inteligente- Latin America. It also contributed thinking towards a 5-year Turing AI Fellowship (https://www.turing.ac.uk/people/researchers/ai-fellows). Has led to invitations for talks and panels on data science for mental health.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -