Scientists build machine learning model for detecting early signs of depression in text

A new machine learning model can detect early signs of depression in written text like Twitter posts, according to a study by University of Alberta computing scientists. "The outcome of our study is that we can build useful predictive models that can accurately identify depressive language," said graduate student Nawshad Farruque, who designed the model to identify linguistic clues in everyday communication. "While we are using the model to identify depressive language on Twitter, (it) can be easily applied to text from other domains for detecting depression." The English-language model was developed using samples of writing by individuals who identify as depressed on online depression forums. The machine learning algorithm was then trained to identify depressive language in tweets.