Ansley J Kunnath, Daniel E Sack, Consuelo H Wilkins
Relative predictive value of sociodemographic factors for chronic diseases among All of Us participants: a descriptive analysis
BMC public health
suiciderelated media content preventive harmful effects depending specific content proactive media screening suicide prevention hampered scarcity machine learning approaches detect specific characteristics news reports study applied machine learning label large quantities broadcast tv radio media data according media recommendations reporting suicide manually labeled 2519 english transcripts 44 broadcast sources oregon washington usa published april 2019 march 2020 conducted content analysis media reports regarding content characteristics trained benchmark machine learning models including majority classifier approaches based word frequency tfidf linear svm deep learning model bert applied models selection simple eg focus suicide death subsequently putatively complex tasks eg determining main focus text 14 categories tfidf svm bert clearly better naive majority classifier characteristics test dataset used model training f1scores ie harmonic mean precision recall ranged 090 celebrity suicide 058 identification main focus media item model performance depended strongly number training samples available much less assumed difficulty classification task study demonstrates machine learning models achieve satisfactory results classifying suiciderelated broadcast media content including multiclass characteristics long enough training samples available developed models enable future largescale screening investigations broadcast media