Every day 22 U.S. veterans take their own lives. In 2012, suicides among active members of the military reached 349, exceeding those killed on duty. Many experts say the military culture makes those at risk less likely to ask for help or admit they are suicidal.
Every day 22 U.S. veterans take their own lives. In 2012, suicides among active members of the military reached 349, exceeding those killed on duty. Many experts say the military culture makes those at risk less likely to ask for help or admit they are suicidal.
Big data may be able to help prevent these suicides from occurring, according to a new study called the Durkheim Project. This new study analyzed real-time data from social networks, Facebook, Twitter and LinkedIn accounts from volunteer veterans and active duty personnel.
The study looked for linguistic cues from social media interaction – words, phases or behavior – that may someday help healthcare workers identify warning signs and help suicidal patients before it’s too late.
After the accumulated social information is captured it will be safeguarded by HIPAA standards and analyzed by various computer programs. No diagnosis or intervention will be given. Participants are welcome to drop out of the study at anytime.
While the results of this study won’t be published for some time, and privacy prohibits discussion of the specific records themselves, the project hopes to point out that initial patterns are not explainable as simply words, but as stories and language within the words. For example, people who are suicidal usually include words related to agitation and inward blame. These patterns are being closely watched.
Identifying suicidal patients in the military population can be difficult. Past studies found that approximately 44 percent of this demographic had visited their primary care physician and 20 percent visited a mental health care worker a month before their death.
The help of big data from social media and continuous monitoring of behavioral intent this new study can help prevent unnecessary deaths.