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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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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 |
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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 |
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