Instagram posts give friends a peek into your personal life, but new research shows they may also provide a snapshot of your mental health. 
 
 Photos taken, edited, and shared to Instagram can show signs of depression through filters, faces, and colors, according to Harvard University’s Andrew Reece and Christopher Danforth from the University of Vermont. 
 
 Computers trained on signals like hue, facial recognition, and saturation were also better at predicting depression than average humans who analyzed photo attributes like happiness, sadness, and likability. Further results show that through this method, depression can be predicted even before an individual receives a diagnosis. Algorithmically generated results had a more successful diagnostic success rate for depression than that of general practitioners. 
 
 Predictive markers of depression in Instagram photos wouldn’t be used for treatment; rather, they could potentially become part of procedures to catch symptoms of depression early and prompt people to get care sooner. 
 
 “We foresee a more mature version of this tool being used more in the context of screening and assessment, rather than treatment,” Danforth and Reece explained in an email. “The algorithm we used looks for complex, systemic patterns across many data points to infer clues about individual psychology. If someone posts a dark, bluish photo to Instagram, it shouldn’t necessarily be a red flag for their therapist—that person could just like photos of whales, or blueberries.” 
 
 The research, a pre-print study published on arxiv.org that has not yet been peer-reviewed, analyzed almost 44,000 Instagram photos from 166 individuals. Surveys were assigned to depressed and healthy individuals; depressed people were asked to share their mental health history, including the date of their diagnosis. 
 
 Once connected to Instagram, researchers analyzed photos in two different ways: Workers on Mechanical Turk were asked to rate images on a scale of zero to five based on how likable, happy, interesting, and sad they were. Then a computer program analyzed pictures based on the number of faces in each photo, colorization, vividness, and brightness of images to find out what image qualities apply to photos shared by depressed individuals. 
 
 Researchers found darker, bluer, and more faded images predicted depression, and the more comments a post received increased the likelihood of being posted by a depressed person; more likes, however, signified the opposite. The “Inkwell” filter was the most common filter used by depressed participants, who were also less likely to apply filters at all, while healthy individuals favored “Valencia” and warmer tones. 
 
 Andrew Reece, Christopher Danforth 
 
 In the comparison below, the image on the right contains the type of photo features exhibited by someone with depression—darker, grayer, and bluer, researchers explained. 
 
 Andrew Reece, Christopher Danforth 
 
 While computers could pick up on the markers of depression, you probably won’t be able to recognize symptoms in a similar way while scrolling through your friends’ Instagram accounts. In the study, human raters (who were unaware of whether or not a person was depressed) exhibited a markedly different photo analysis. 
 
 “Human ratings exhibited extremely low correlation with computational features,” like hues and facial presence, researchers said. “In other words, the bluer, darker, grayer photos weren’t the sad photos, according to the people we asked. So while our computational approach is capable of screening for depression, it uses different information signals than those captured by human judgments of what makes a photo sad.” 
 
 Surprisingly, the study found depressed participants posted more photos with faces, but the number of people in each photo was lower. Danforth said they’re still considering what this finding suggests, but it might indicate smaller social activities. 
 
 Machine learning is increasingly used in clinical settings, and researchers continue to investigate how artificial intelligence can impact mental health. For instances, therapy bots are used to diagnose, assess, and comfort patients with mental disorders. Early tests showed that some people feel more comfortable talking to a screen about mental health, post-traumatic stress disorder, and anxiety as opposed to human therapists. AI is in development that detects physical ailments, too. Doctors are reportedly using Watson, IBM’s cognitive system, to predict cancer in patients. 
 
 However, researchers cautioned against making a formal comparison to human physicians, or thinking that machine learning will replace your therapist. The computer’s findings were not directly tested against clinicians analyzing the same photos of depressed patients. 
 
 “The intuition and experience of human physicians are still very important, especially in mental health care settings, and machine learning methods seem to work best when they complement, rather than replace, human knowledge,” they said. “In that sense, ‘augmenting’, rather than ‘displacing,’ might be a better way to describe the new approaches to human-computer interaction that are advancing health care.” 
 
 So perhaps in the future, a computer might analyze your mental health before you visit a doctor’s office, and once markers are recognized, a real human being can begin treatment. ||||| Instagram can be a good indicator of a person’s mental health. 
 
 At least that’s what two researchers, Harvard University’s Andrew Reece and the University of Vermont’s Chris Danforth, have written in a recent report. The study was based on 166 volunteers who were recruited from Amazon Mechanical Turk, where you can make money by completing “Human Intelligence Tasks,” and it looked at their entire Instagram histories, which came out to about 43,950 photos. 
 
 Participants’ levels of depression were determined using a standardized clinical depression survey. They were then asked about their social media habits and history of depression diagnosis. The researchers then analyzed their Instagram photos by looking at colors, brightness, and faces. 
 
 Those who were depressed tended to post photos with increased hue, decreased brightness, and decreased color saturation. Overall, their photos were “bluer, grayer, and darker.” They also tended to post more frequently and use more Instagram filters, the most popular one for depressed participants being Inkwell, which turns a photo black and white. The most popular filter for volunteers who weren’t depressed was Valencia. 
 
 The depressed participants were more likely to post photos that include faces, but their pictures tended to have fewer faces than Instagram users who weren’t depressed. Reece and Danforth write that this could be an indiction that depressed people generally have smaller social circles. 
 
 The study also asked participants to rank others’ photos based on four subjective elements: happiness, sadness, likability, and “interestingness.” The latter two didn’t appear to be significant indicators of depression and, predictably, depressed participants’ photos were ranked higher on the “sadness” scale. 
 
 “These findings suggest new avenues for early screening and detection of mental illness,” Reece and Danforth write, though they don’t elaborate on exactly how that would be implemented. Reece added in an email to Fortune that this is “just a first step, and a lot of work needs to be done before we can get to the point where an algorithm can reliably augment professional diagnostic decision-making.” 
 
 It’s notable that this study was posted to arXiv, Cornell University’s open access website that lets researchers publish findings before they appear in a formal publication. Reece told Fortune that the study has been submitted for publication and it is currently undergoing peer review. He added that feedback from both fellow researchers and medical professionals has been “generally positive.” ||||| Your Instagram filter may reveal more than you realize about your mental health. 
 
 Researchers from Harvard and the University of Vermont have found that Instagram photos can be analyzed to screen for depression. The scientists used the photos' attributes, including brightness and color, to correctly identify which participants suffered from depression at a better rate than the typical physician. 
 
 Photos with decreased brightness, decreased saturation and increased hue indicated depression. The computer correctly identified 70% of the instances of depression. 
 
 Valencia was the most popular filter with users who were not depressed, whereas depressed users were most likely to use Inkwell. Valencia lightens photos whereas Inkwell converts images to black and white. 
 
 The findings point to an unobtrusive, inexpensive way to use social media to detect health issues. 
 
 "It's all a good thing," said Michael Thase, the director of the Mood and Anxiety Program at the University of Pennsylvania. "It's only not a good thing when the information would be sold to somebody who might make a buck from interfacing with depressed people." 
 
 Thase suggested that in the future we may opt in to receive screening feedback from social media. A user who gave consent would have their photos scanned for signs of potential illnesses. 
 
 Related: Instagram Stories is Facebook's latest Snapchat clone 
 
 "Did you know your choice of hues and colors go along with people who are prone to depression. Would you like to know more?" Thase suggested as a possible private message that could be sent to users who appeared depressed. 
 
 To reach their conclusions, the researchers analyzed roughly 13,000 photos from 166 Instagram users, some of whom were clinically depressed. 
 
 A computer system was trained by looking at the photos' brightness, vividness, hue and whether an Instagram filter was used. The computer learned to make predictions of depression after comparing the photos of depressed individuals, and those who are not depressed. 
 
 Related: Health care will cost you $260,000 in retirement 
 
 The research is the latest example of how useful insights can be gleaned from the digital footprints we leave online. Tech companies will increasingly have opportunities to assist patients and their health care providers, according to Thase. 
 
 So how would such a program appear in the real world? It would likely start with a tech company inviting users to participate, and encouraging the traditional medical community to endorse it. Information could potentially be automatically shared with a patient's physician, provided the patient opts in. 
 
 Earlier this year, Microsoft (MSFT, Tech30) researchers showed that a person's Web search history could sometimes predict an upcoming diagnosis of pancreatic cancer.
– Your Instagram account may know you're depressed before you do, according to a new study out of Harvard and the University of Vermont. The Daily Dot reports researchers taught a computer to analyze aspects of Instagram photos, such as colors, brightness, and faces, then set in to analyzing nearly 44,000 posts from 166 people. The Instagram users in the study took a clinical depression survey and were asked about any history of depression, according to Fortune. CNN reports the computer program was able to correctly detect depression from a person's Instagram account 70% of the time. That's better than humans looking at Instagram photos and better than the success rate of general practitioners diagnosing depression. Researchers found the Instagram posts of depressed people tended to be less saturated, darker, and more blue. Depressed people tended to use the Inkwell filter most often (non-depressed people preferred Valencia) and got more comments on their posts (non-depressed people got more likes). Depressed people also posted more photos of faces but had fewer faces per photo. Because the computer program was able to detect depression in Instagram users before the were even diagnosed with it, researchers believe it could be a new way to screen people for depression before they start showing more serious symptoms and get them help earlier. (This party drug may soon treat depression.)