![]() ![]() Individuals’ self-portrayal on social media may then be influenced by this identification. Hypothesis 1 is a necessary first step, as it addresses an unanswered basic question: Is depression detectable in Instagram posts? On finding support for Hypothesis 1, a natural question arises: Is depression detectable in Instagram posts, before the date of first diagnosis? After receiving a depression diagnosis, individuals may come to identify with their diagnosis. We also counted the number of comments and likes each post received as measures of community engagement, and used posting frequency as a metric for user engagement. On this premise, we used a face detection algorithm to analyze Instagram posts for the presence and number of human faces in each photograph. As Instagram is used to share personal experiences, it is reasonable to infer that posted photos with people in them may capture aspects of a user’s social life. ![]() We also tracked the use of Instagram filters, which allow users to modify the color and tint of a photograph.ĭepression is strongly associated with reduced social activity. These findings motivated us to include measures of hue, saturation, and brightness in our analysis. In addition, Barrick, Taylor, & Correa found a positive correlation between self-identification with depression and a tendency to perceive one’s surroundings as gray or lacking in color. By contrast, depressed individuals were found to prefer darker, grayer colors. In studies associating mood, color, and mental health, healthy individuals identified darker, grayer colors with negative mood, and generally preferred brighter, more vivid colors. We incorporated only a narrow subset of possible features into our predictive models, motivated in part by prior research into the relationship between mood and visual preferences. Instagram metadata offers additional information: Did the photo receive any comments? How many ‘Likes’ did it get? Finally, platform activity measures, such as usage and posting frequency, may also yield clues as to an Instagram user’s mental state. The content of photographs can be coded for any number of characteristics: Are there people present? Is the setting in nature or indoors? Is it night or day? Image statistical properties can also be evaluated at a per-pixel level, including values for average color and brightness. Photographs posted to Instagram offer a vast array of features that might be analyzed for psychological insight. Instagram posts made by individuals diagnosed with depression can be reliably distinguished from posts made by healthy controls, using only measures extracted computationally from posted photos and associated metadata. Our goal was to successfully identify and predict markers of depression in Instagram users’ posted photographs. 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. employed a time-consuming qualitative coding method which the authors acknowledged made it ‘impossible to qualitatively analyze’ Instagram data at scale (p.4). only attempted to correlate Instagram usership with depressive symptoms, and Andalibi et al. 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. Instagram members currently contribute almost 100 million new posts per day, and Instagram’s rate of new users joining has recently outpaced Twitter, YouTube, LinkedIn, and even Facebook. There is good reason to prioritize research into Instagram analysis for health screening. In this report, we introduce a methodology for analyzing photographic data from Instagram to predictively screen for depression. 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. ![]() Predictive screening methods have successfully analyzed online media to detect a number of harmful health conditions. The advent of social media presents a promising new opportunity for early detection and intervention in psychiatric disorders. ![]()
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