A computer program has been developed that is able to correctly identify depressed individuals from their social media photos 70% of the time, according to a study involving 166 users of a popular social media app. In comparison, previous research has shown that general practitioners can make a correct unassisted diagnosis of depression 42% of the time. The research is published in the open access journal EPJ Data Science.
Dr Christopher Danforth, study co-author from the University of Vermont, said: “Our analysis of user accounts from a popular social media app revealed that photos posted by people diagnosed with depression tended to be darker in color, received more comments from the community, were more likely to contain faces and less likely to have a filter applied. When they did select a filter they were more likely to use the filter that converted color images to black and white. People diagnosed with depression also posted at a higher frequency compared to non-depressed individuals.”
Dr Danforth added: “With an increasing share of our social interactions happening online, the potential for algorithmic identification of early-warning signs for a host of mental and physical illnesses is enormous. Imagine an app you can install on your phone that pings your doctor for a check-up when your behavior changes for the worse, potentially before you even realize there is a problem.”
The researchers used the computer program to analyse 43,950 photos, following recruitment of 166 users of a popular social media app, including 71 people that had a clinical diagnosis of depression. The program scoured the photos for details that were associated with healthy and depressed individuals. This information was then used to see if the program could predict who would go on to be diagnosed with depression by only looking at photos that were posted before their diagnosis.
Dr Andrew Reece, study co-author from Harvard University, said: “Although we had a relatively small sample size, we were able to reliably observe differences in features of social media posts between depressed and non-depressed individuals. Importantly, we also demonstrate that the markers of depression can be observed in posts made prior to the person receiving a clinical diagnosis of depression.”
The study is limited by the generalization of the term “depression” in the data collection process. Depression is a complex, multifaceted illness and is frequently present with other health conditions. Future research should seek to address the reliability of the computer model against more finely tuned definitions of depressive disorders, according to the researchers. Furthermore, a total of 509 participants were initially recruited, but 43% were dropped from the study because they did not consent to sharing their social media data. This raises concerns around data privacy which will need to be addressed should a similar model be developed for widespread clinical use.
T: +44 (0)20 3192 2722
M: +44 (0)75 4079 9187
Notes to editor:
1. Research article:
Instagram photos reveal predictive markers of depression
Andrew G. Reece and Christopher M. Danfoth
EPJ Data Science Aug 2017
After the embargo lifts, the article will be available at the journal website here: https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0110-z
Please name the journal in any story you write. If you are writing for the web, please link to the article. All articles are available free of charge, according to BioMed Central's open access policy.
2. The 21st century is currently witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already largely transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly. EPJ Data Science offers a publication platform to address this evolution by bringing together all academic disciplines concerned with the same challenges.
3. SpringerOpen, launched in June 2010, includes Springer’s portfolio of 200+ peer-reviewed fully open access journals across all areas of science. In August 2012, due to the growing demand for open access and the success of our SpringerOpen journals, we expanded our offering to open access books. Published under the SpringerOpen brand they complement our established open access journal portfolio. SpringerOpen journals and books are made freely and permanently available online immediately upon publication. They are subject to high-level peer review, author and production services ensuring quality and reliability of the work. Authors publishing with SpringerOpen retain the copyright to their work, licensing it under a Creative Commons license. To cover the cost of the publication process, all SpringerOpen journals and books charge an open access fee.