{"id":4956,"date":"2018-10-17T11:58:05","date_gmt":"2018-10-17T07:58:05","guid":{"rendered":"http:\/\/www.uaebarq.ae\/en\/?p=4956"},"modified":"2018-10-17T11:58:05","modified_gmt":"2018-10-17T07:58:05","slug":"using-facebook-to-predict-depression","status":"publish","type":"post","link":"https:\/\/www.uaebarq.ae\/en\/2018\/10\/17\/using-facebook-to-predict-depression\/","title":{"rendered":"Using Facebook to predict depression"},"content":{"rendered":"

New research uses over half a million Facebook status updates to predict depression diagnoses in people at risk.<\/strong><\/p>\n

Depression\u00a0is one of the most widespread\u00a0mental health\u00a0problems in the world, with a large number of people\u00a0having experienced at least one major depressive episode in their lifetimes.<\/p>\n

Worldwide, the World Health Organization (WHO)\u00a0estimate\u00a0that unipolar depressive disorders will be “the leading cause of the global burden of disease” by 2030.<\/p>\n

Currently, however, the condition remains underdiagnosed, particularly among\u00a0young people\u00a0and\u00a0men. New research aims to help create better screening and diagnostic tools for depression by using the information provided by social media.<\/p>\n

Researchers, jointly led by Johannes Eichstaedt, founding research scientist at the World Well-Being Project (WWBP) in Philadelphia, PA, and H. Andrew Schwartz, a principal investigator of the WWBP, used an algorithm to analyze social media data from consenting users and picked out linguistic cues that might predict depression.<\/p>\n

The team published their\u00a0findings\u00a0in the journal\u00a0Proceedings of the National Academy of Sciences<\/em>. Johannes Eichstaedt is the first author of the paper.<\/p>\n

Analyzing half a million Facebook posts<\/h2>\n

Eichstaedt and colleagues analyzed data from almost 1,200 people who agreed to provide their Facebook status updates and their electronic medical records. Of these participants, only 114 had a history of depression.<\/p>\n

Study co-author Raina Merchant says, “For this project, all individuals [have] consented, no data is collected from their network, the data is anonymized, and the strictest levels of privacy and security are adhered to.”<\/p>\n

Then, for every person who had received a diagnosis of depression in their lives, the researchers matched another five controls who had not. In this way, the researchers matched 683 people.<\/p>\n

The scientists fed the information into an algorithm. In total, Eichstaedt and colleagues analyzed 524,292 Facebook status updates from both people who had a history of depression and from those who did not.<\/p>\n

The updates were collected from the years leading up to a diagnosis of depression and for a similar period for depression-free participants.<\/p>\n

By modeling conversations on 200 topics, the researchers determined a range of so-called depression-associated language markers, which depicted emotional and cognitive cues, including “sadness, loneliness, hostility, rumination, and increased self-reference” \u2014 that is an increased use of first-person pronouns, such as “I” or “me.”<\/p>\n

Eichstaedt and team proceeded to examine how often people with depression used these markers, compared with controls.<\/p>\n

Social media as depression diagnostic tool<\/h2>\n

The researchers found that the linguistic markers could predict depression with “significant” accuracy up to 3 months before the person receives a formal diagnosis.<\/p>\n

“Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures,” conclude the authors.<\/p>\n

The study’s first author also comments on the findings, saying, “The hope is that one day, these screening systems can be integrated into systems of care.”<\/strong><\/p>\n

“This tool raises yellow flags; eventually the hope is that you could directly funnel people it identifies into scalable treatment modalities,” Eichstaedt continues.<\/p>\n

The researcher goes on to compare their social media algorithm with a DNA analysis. “Social media data contain markers akin to the genome,” Eichstaedt says.<\/p>\n

“With surprisingly similar methods to those used in genomics, we can comb social media data to find these markers. Depression appears to be something quite detectable in this way; it really changes people’s use of social media in a way that something like skin disease or\u00a0diabetes\u00a0doesn’t.”<\/p>\n

“<\/span>[Social media] may turn out to be an important tool for diagnosing, monitoring, and eventually treating it. Here, we’ve shown that it can be used with clinical records, a step toward improving mental health with social media.”<\/p>\n

–<\/p>\n

MedicalNews<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

New research uses over half a million Facebook status updates to predict depression diagnoses in people at risk. Depression\u00a0is one of the most widespread\u00a0mental health\u00a0problems in the world, with a large number of people\u00a0having experienced at least one major depressive episode in their lifetimes. Worldwide, the World Health Organization (WHO)\u00a0estimate\u00a0that unipolar depressive disorders will be …<\/p>\n","protected":false},"author":2,"featured_media":4957,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-4956","post","type-post","status-publish","format-standard","has-post-thumbnail","","category-health"],"_links":{"self":[{"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/posts\/4956","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/comments?post=4956"}],"version-history":[{"count":1,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/posts\/4956\/revisions"}],"predecessor-version":[{"id":4958,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/posts\/4956\/revisions\/4958"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/media\/4957"}],"wp:attachment":[{"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/media?parent=4956"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/categories?post=4956"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.uaebarq.ae\/en\/wp-json\/wp\/v2\/tags?post=4956"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}