Benjamin J Walker, Adam K Townsend, Alexander K Chudasama, Andrew L Krause
VisualPDE: Rapid Interactive Simulations of Partial Differential Equations
Bulletin of mathematical biology
effective suicide risk assessments interventions vital suicide prevention although assessing risks best done health care professionals people experiencing suicidal ideation may seek help hence machine learning ml computational linguistics provide analytical tools understanding analyzing risks therefore facilitates suicide intervention prevention study aims explore using statistical analyses ml whether computerized language analysis could applied assess better understand persons suicide risk social media used university maryland suicidality dataset comprising text posts written users n866 mental healthrelated forums reddit user classified suicide risk rating low moderate severe either medical experts crowdsourced annotators denoting estimated likelihood dying suicide language analysis linguistic inquiry word count lexicon assessed sentiment thinking styles part speech whereas readability explored using textstat library mannwhitney u test identified differences atrisk low moderate severe risk norisk users meanwhile kruskalwallis test spearman correlation coefficient used granular analysis risk levels identify redundancy respectively ml experiments gradient boost random forest support vector machine models trained using 10fold cross validation area receiver operator curve f1score primary measures finally permutation importance uncovered features contributed models decisionmaking statistically significant differences p05 identified atrisk 671866 775 norisk groups 195866 225 true crowd expertannotated samples overall atrisk users higher median values variables authenticity firstperson pronouns negation notable exception clout indicated atrisk users less likely engage social posturing high positive correlation 084 present part speech variables implied redundancy demonstrated utility aggregate features ml models performed similarly area curve 066068 however random forest gradient boost models noticeably better f1score 065 062 support vector machine 052 features contributed ml models authenticity clout negative emotions summary statistical analyses found linguistic features associated suicide risk social posturing eg authenticity clout firstperson singular pronouns negation increased understanding behavioral thought patterns social media users provided insights mechanisms behind ml models also demonstrated applicative potential ml assisting health care professionals assess manage individuals experiencing suicide risk