Instagram photos reveal predictive markers of depression
08.08.2017
Andrew G Reece and Christopher M Danforth
▻https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0110-z
Abstract
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.
1 Introduction
The advent of social media presents a promising new opportunity for early detection and intervention in psychiatric disorders. Predictive screening methods have successfully analyzed online media to detect a number of harmful health conditions [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. All of these studies relied on text analysis, however, and none have yet harnessed the wealth of psychological data encoded in visual social media, such as photographs posted to Instagram. In this report, we introduce a methodology for analyzing photographic data from Instagram to predictively screen for depression.
There is good reason to prioritize research into Instagram analysis for health screening. Instagram members currently contribute almost 100 million new posts per day [12], and Instagram’s rate of new users joining has recently outpaced Twitter, YouTube, LinkedIn, and even Facebook [13]. A nascent literature on depression and Instagram use has so far either yielded results that are too general or too labor-intensive to be of practical significance for predictive analytics [14, 15]. In particular, Lup et al. [14] only attempted to correlate Instagram usership with depressive symptoms, and Andalibi et al. [15] employed a time-consuming qualitative coding method which the authors acknowledged made it ‘impossible to qualitatively analyze’ Instagram data at scale (p.4). In our research, we incorporated an ensemble of computational methods from machine learning, image processing, and other data-scientific disciplines to extract useful psychological indicators from photographic data. Our goal was to successfully identify and predict markers of depression in Instagram users’ posted photographs.
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pris d’ici: ▻https://seenthis.net/messages/621331
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#social_media #machine_learning #photographie
#depression #psychologie #santé_psychique
#numérique