At http://mashable.com/2016/06/26/suicide-prevention-social-media/#o0uEnngJ3aq3"Each word you use shows a tiny fraction of a bit of a clue in terms of who you are."BY REBECCA RUIZJUN 26, 2016IMAGE: VICKY LETA / MASHABLETake a moment to look at your emoji keyboard. Scroll through the angry face, ghost, stiletto, doughnut, flashlight and cigarette until you reach the hearts. There it is: love. Amid the mundane and humorous, those vibrant, colorful little shapes can easily become a rapid-fire display of affection to a friend, parent or partner. But notice, too, the broken and blue hearts, and their restrained reminders of sadness, loneliness or grief.It turns out that these little characters are more important than we could imagine. Linguists, psychologists and computer scientists are discovering that what we collectively share on social media, and when, can signal information about our mental health. Some of these researchers believe machine learning, algorithms and mathematical analysis can give health care providers tools to help solve one of our most intractable public health epidemics: suicide. In an underfunded field where saving every at-risk life can feel like an impossible goal, this technology promises to give public health experts and clinicians a novel tool to predict suicide risk. Given the rising rate of suicide in the United States, that kind of prevention can't come quickly enough. The clues in your tweets Predicting suicide risk is exactly what Glen Coppersmith, a data scientist and psychologist, has set out to do. That mission, he says, is an urgent one. Data released in April from the Centers for Disease Control and Prevention revealed an alarming rise in suicides among both genders and in every age group between 10 and 74. Between 1999 and 2014, the suicide rate in the U.S. increased 24%, to 13 per 100,000 people. As founder and CEO of the startup mental health analytics company Qntfy, Coppersmith is using machine learning to design algorithms that identify trends in human communication. That concept is well-established science, and it's one you rely on every time you misspell a word and know autocorrect will catch (or worsen) the mistake. Coppersmith belongs to a small group of researchers who believe this technology can be put in the service of detecting a person's risk of mental illness and suicidal behavior. In his most recent study, published this month at an annual North American Association for Computational Linguistics meeting, he and his co-authors estimated the emotional content of tweets from hundreds of users who had talked openly about a suicide attempt, and tweets from a control group that did not display suicidal thoughts or feelings. Using a model designed to predict suicide risk in the former group, the researchers discovered their algorithm worked and picked up on surprising patterns, including one on how those emoji hearts can say more than we'd ever casually expect. While nearly everyone in their sample included emoji in their tweets, Coppersmith and his co-authors noticed that some in the group that talked about attempting suicide employed a narrow range of emoji representing sadness more frequently, compared to typical users of the same age and gender.