UAlberta Science News, Folio, Techxplore and Global News reported that the model was developed using writing samples of self-identified depressed individuals, as detailed in the paper “Augmenting Semantic Representation of Depressive Language: From Forums to Microblogs” published at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML-PKDD).
“The outcome of our study is that we can build useful predictive models that can accurately identify depressive language,” said Farruque in the interview. “While we are using the model to identify depressive language on Twitter, the model can be easily applied to text from other domains for detecting depression.”
As depression globally affects more than 264 million people of all ages (according to the World Health Organization), the applications for an algorithm of this nature are immense; from detecting early signs of depression in youth through social media posts to monitoring the mental health of seniors through conversational chatbots.
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Jun 27th 2022
2100 guests, nearly 60 events and over 80 speakers — all to celebrate 20 years of AI excellence. Thanks for making the inaugural AI week a smashing success!
Jun 23rd 2022
AI4Good's Demo Day showcased 14 innovative ideas to use AI for societal good. Come learn more about the teams taking part in this year's program.
Jun 23rd 2022
On April 1, Greg Coulombe & Horace Chan of Intuit Edmonton presented “Conversational AI at Intuit" at the AI Seminar.
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