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Instagram photos reveal predictive markers of depression

EPJ data science, 2017-12, Vol.6 (1), p.15-12, Article 15 [Peer Reviewed Journal]

The Author(s) 2017 ;The Author(s) 2017. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2193-1127 ;EISSN: 2193-1127 ;DOI: 10.1140/epjds/s13688-017-0110-z

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Title:
Instagram photos reveal predictive markers of depression
Author: Reece, Andrew G ; Danforth, Christopher M
Subjects: Complexity ; computational social science ; Computer Appl. in Social and Behavioral Sciences ; Computer Science ; Data-driven Science ; depression ; Face recognition ; Machine learning ; Mental disorders ; Modeling and Theory Building ; psychology ; Regular Article ; social media
Is Part Of: EPJ data science, 2017-12, Vol.6 (1), p.15-12, Article 15
Description: Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 2193-1127
EISSN: 2193-1127
DOI: 10.1140/epjds/s13688-017-0110-z
Source: SpringerOpen
ProQuest Central
DOAJ Directory of Open Access Journals

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Instagram photos reveal predictive markers of depression

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