Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

Scott Braithwaite · Christophe G. Giraud-Carrier · Joshua H. West · Carl Lee Hanson

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Mental Health via Writing Suicide and Self-Harm Studies Sentiment Analysis and Opinion Mining

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.

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2010The interpersonal theory of suicide.1990Suicide as escape from self.1993The science of prevention: A conceptual framework and some directions for a national research program.2008Suicidal desire and the capability for suicide: Tests of the interpersonal-psychological theory of suicidal behavior among adults.2002Screening for Depression in Adults: A Summary of the Evidence for the U.S. Preventive Services Task Force 2015A review of depression and suicide risk assessment using speech analysis 2014Quantifying Mental Health Signals in Twitter 2001Word Use in the Poetry of Suicidal and Nonsuicidal Poets

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2017Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression 2018Detection of suicide-related posts in Twitter data streams 2022Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models 2018Detecting Suicidal Ideation on Forums: Proof-of-Concept Study 2020Using Social Media for Mental Health Surveillance

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