O'Dea, Bridianne; Wan, Stephen; Batterham, Philip J.; Calear, Alison L.; Paris, Cecile; Christensen, Helen
Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. However, validated and reliable methods are not yet fully established. This study aimed to examine whether the level of concern for a suicide-related post on Twitter could be determined based solely on the content of the post, as judged by human coders and then replicated by machine learning. From 18th February 2014 to 23rd April 2014, Twitter was monitored...[Show more]
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