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Instagram photos reveal predictive markers of depression
by
Reece, Andrew G
, Danforth, Christopher M
in
Complexity
/ computational social science
/ Computer Appl. in Social and Behavioral Sciences
/ Computer Science
/ Data science
/ Data-driven Science
/ depression
/ Emotions
/ Face recognition
/ Happiness
/ Hypotheses
/ Machine learning
/ Medical screening
/ Mental depression
/ Mental disorders
/ Metadata
/ Modeling and Theory Building
/ psychology
/ Regular Article
/ social media
/ Social networks
2017
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Instagram photos reveal predictive markers of depression
by
Reece, Andrew G
, Danforth, Christopher M
in
Complexity
/ computational social science
/ Computer Appl. in Social and Behavioral Sciences
/ Computer Science
/ Data science
/ Data-driven Science
/ depression
/ Emotions
/ Face recognition
/ Happiness
/ Hypotheses
/ Machine learning
/ Medical screening
/ Mental depression
/ Mental disorders
/ Metadata
/ Modeling and Theory Building
/ psychology
/ Regular Article
/ social media
/ Social networks
2017
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Instagram photos reveal predictive markers of depression
by
Reece, Andrew G
, Danforth, Christopher M
in
Complexity
/ computational social science
/ Computer Appl. in Social and Behavioral Sciences
/ Computer Science
/ Data science
/ Data-driven Science
/ depression
/ Emotions
/ Face recognition
/ Happiness
/ Hypotheses
/ Machine learning
/ Medical screening
/ Mental depression
/ Mental disorders
/ Metadata
/ Modeling and Theory Building
/ psychology
/ Regular Article
/ social media
/ Social networks
2017
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Journal Article
Instagram photos reveal predictive markers of depression
2017
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Overview
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
Springer Berlin Heidelberg,Springer Nature B.V,SpringerOpen
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