title: Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.
abstract: BACKGROUND
  One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time.
  
  
  OBJECTIVE
  Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population.
  
  
  METHODS
  Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk.
  
  
  RESULTS
  Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%).
  
  
  CONCLUSIONS
  Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.
authors.affiliation: Computational Health Science Research Group, Department of Psychology, Brigham Young University, Provo, UT, United States.
authors.forename: Scott R
authors.identifier: http://orcid.org/0000-0002-2896-9599
authors.initials: SR
authors.lastname: Braithwaite
authors.forename: Christophe
authors.identifier: http://orcid.org/0000-0002-4129-3239
authors.initials: C
authors.lastname: Giraud-Carrier
authors.forename: Josh
authors.identifier: http://orcid.org/0000-0003-1086-3865
authors.initials: J
authors.lastname: West
authors.forename: Michael D
authors.identifier: http://orcid.org/0000-0002-8865-3668
authors.initials: MD
authors.lastname: Barnes
authors.forename: Carl Lee
authors.identifier: http://orcid.org/0000-0002-0792-3372
authors.initials: CL
authors.lastname: Hanson
keywords: machine learning; social media; suicide; twitter