Your Instagram posts may hold a key to your mental health
The photos you share online may hold clues to your mental health, suggests a new research.
Photos reflect your style and your quirks – photos can also serve as a form of self-expression or a record of travel.
They might convey even more than you realise – key to your mental health.
According to the study, published in the journal ‘EPJ Data Science’, Instagram users with a history of depression seem to present the world differently from their peers, ranging from colours and faces in their photos to the enhancements they make before posting them.
‘People in our sample who were depressed tended to post photos that, on a pixel-by-pixel basis, were bluer, darker and grayer on average than healthy people,’ said Andrew Reece, a postdoctoral researcher at Harvard University and co-author of the study with Christopher Danforth, a professor at the University of Vermont.
The researchers identified participants as ‘depressed’ or ‘healthy’ based on whether they reported having received a clinical diagnosis of depression in the past.
Machine-learning tools were used by the researchers to find patterns in the photos and to create a model predicting depression by the posts. They used software to analyse each photo’s hue, colour saturation and brightness, as well as the number of faces it contained. They also collected information about the number of posts per user and the number of comments and likes on each post.
The researchers found that depressed participants used fewer Instagram filters. When these users did add a filter, they tended to choose ‘Inkwell’, which drains a photo of its colour, making it black and-white.
Users who were not diagnosed as depressed tended to prefer ‘Valencia’, which lightens a photo’s tint.
Depressed participants were more likely to post photos containing a face. But when the healthier ones posted photos with faces, they tended to feature more of them.
Though they warned that their findings may not apply to all Instagram users, Reece and Danforth argued that the results suggest that a similar machine-learning model could someday prove useful in conducting or augmenting mental health screenings.
