Cargando…

Forecasting the onset and course of mental illness with Twitter data

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic...

Descripción completa

Detalles Bibliográficos
Autores principales: Reece, Andrew G., Reagan, Andrew J., Lix, Katharina L. M., Dodds, Peter Sheridan, Danforth, Christopher M., Langer, Ellen J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5636873/
https://www.ncbi.nlm.nih.gov/pubmed/29021528
http://dx.doi.org/10.1038/s41598-017-12961-9
_version_ 1783270528161677312
author Reece, Andrew G.
Reagan, Andrew J.
Lix, Katharina L. M.
Dodds, Peter Sheridan
Danforth, Christopher M.
Langer, Ellen J.
author_facet Reece, Andrew G.
Reagan, Andrew J.
Lix, Katharina L. M.
Dodds, Peter Sheridan
Danforth, Christopher M.
Langer, Ellen J.
author_sort Reece, Andrew G.
collection PubMed
description We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (N(users) = 174, N(tweets) = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.
format Online
Article
Text
id pubmed-5636873
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-56368732017-10-18 Forecasting the onset and course of mental illness with Twitter data Reece, Andrew G. Reagan, Andrew J. Lix, Katharina L. M. Dodds, Peter Sheridan Danforth, Christopher M. Langer, Ellen J. Sci Rep Article We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (N(users) = 174, N(tweets) = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness. Nature Publishing Group UK 2017-10-11 /pmc/articles/PMC5636873/ /pubmed/29021528 http://dx.doi.org/10.1038/s41598-017-12961-9 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Reece, Andrew G.
Reagan, Andrew J.
Lix, Katharina L. M.
Dodds, Peter Sheridan
Danforth, Christopher M.
Langer, Ellen J.
Forecasting the onset and course of mental illness with Twitter data
title Forecasting the onset and course of mental illness with Twitter data
title_full Forecasting the onset and course of mental illness with Twitter data
title_fullStr Forecasting the onset and course of mental illness with Twitter data
title_full_unstemmed Forecasting the onset and course of mental illness with Twitter data
title_short Forecasting the onset and course of mental illness with Twitter data
title_sort forecasting the onset and course of mental illness with twitter data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5636873/
https://www.ncbi.nlm.nih.gov/pubmed/29021528
http://dx.doi.org/10.1038/s41598-017-12961-9
work_keys_str_mv AT reeceandrewg forecastingtheonsetandcourseofmentalillnesswithtwitterdata
AT reaganandrewj forecastingtheonsetandcourseofmentalillnesswithtwitterdata
AT lixkatharinalm forecastingtheonsetandcourseofmentalillnesswithtwitterdata
AT doddspetersheridan forecastingtheonsetandcourseofmentalillnesswithtwitterdata
AT danforthchristopherm forecastingtheonsetandcourseofmentalillnesswithtwitterdata
AT langerellenj forecastingtheonsetandcourseofmentalillnesswithtwitterdata