contribution on Pinterest
Reading: Using Facebook to predict depression
Analyzing half a million Facebook posts
Eichstaedt and colleagues analyzed data from about 1,200 people who agreed to provide their Facebook status updates and their electronic medical records. Of these participants, lone 114 had a history of depression. Study co-author Raina Merchant says, “ For this project, all individuals [ have ] consented, no datum is collected from their network, the datum is anonymized, and the strictest levels of privacy and security are adhered to. ” then, for every person who had received a diagnosis of depressive disorder in their lives, the researchers matched another five controls who had not. In this direction, the researchers matched 683 people. The scientists fed the information into an algorithm. In total, Eichstaedt and colleagues analyzed 524,292 Facebook condition updates from both people who had a history of depression and from those who did not.
The updates were collected from the years leading up to a diagnosis of depression and for a exchangeable menstruation for depression-free participants. By modeling conversations on 200 topics, the researchers determined a stove of alleged depression-associated language markers, which depicted aroused and cognitive cues, including “ sadness, forlornness, hostility, contemplation, and increased self-reference ” — that is an increase function of first-person pronouns, such as “ I ” or “ me. ” Eichstaedt and team proceeded to examine how frequently people with depression used these markers, compared with controls.
Social media as depression diagnostic tool
The researchers found that the linguistic markers could predict depression with “ significant ” accuracy up to 3 months before the person receives a formal diagnosis. “ unobtrusive depression assessment through sociable media of consenting individuals may become feasible as a scalable complement to existing screen and monitoring procedures, ” conclude the authors. 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.” “ This tool raises yellow flags ; finally the hope is that you could directly funnel people it identifies into scalable treatment modalities, ” Eichstaedt continues. The research worker goes on to compare their social media algorithm with a deoxyribonucleic acid analysis. “ social media data contain markers akin to the genome, ” Eichstaedt says. “ With amazingly alike 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 actually changes people ’ sulfur use of social media in a way that something like skin disease or diabetes doesn ’ thymine. ”
“ [ Social media ] may turn out to be an important instrument for diagnosing, monitor, and finally treating it. here, we ’ ve shown that it can be used with clinical records, a step toward improving mental health with sociable media. ” H. Andrew Schwartz
