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UVM Study Ranked Among 2017's Most Popular

Two Roads from EPJ Data Science
Research from August 2017 shows that Instagram photos posted by depressed individuals had values shifted towards those in the right photograph—darker, grayer and bluer—compared with brighter photos posted by healthy individuals. (Photo: EPJ Data Science)

A UVM research study, which discovered Instagram photos hold clues to aid in the early detection of depression, was one of the 20 most popular pieces of academic research in all of 2017, according to a new ranking.

The annual Altmetric Top 100 ranks which pieces of research have caught the public imagination in the last 12 months. To determine 2017’s list, Altmetric tracked over 18.5 million mentions of 2.2 million different pieces of research.

The study by UVM professor Chris Danforth and Andrew Reece of Harvard University, “Instagram photos reveal predictive markers of depression,” came in at No. 17. Danforth is a professor in UVM's Department of Mathematics & Statistics and co-director of the university's Computational Story Lab.

Published Aug. 8 in a leading data-science journal EPJ Data Science, the research was covered by media outlets around the world including The New York TimesUSA TodayQuartz, and nearly 250 others. The study was also mentioned in thousands of tweets, blog posts, and more.

“As in previous years, medical and public health issues have drawn the highest levels of attention,” according to Almetric’s website.

Read more about Danforth’s findings, and view the full Altmetric Top 100 ranking