Presentation Title

The sentiment of depression on Twitter

Presenter's Name(s)

Anne Marie StupinskiFollow

Time

3:00 PM

Location

Silver Maple Ballroom - Engineering & Physical Sciences

Abstract

Major depression is a serious health issue afflicting hundreds of millions of people each year, with many going untreated due to the intense stigma surrounding mental illness. Using the Hedonometer, we analyze the sentiment of messages containing the word ‘depression’ posted to Twitter from 2008-2018. We then use a ‘white-box’ tool developed by our group to reveal the words most responsible for sentiment shifts. In particular, we identify specific dates on which sentiment deviates largely from the baseline in response to corresponding real-world events. The results offer insight into language use associated with depression on Twitter.

Primary Faculty Mentor Name

Chris Danforth

Secondary Mentor NetID

pdodds

Secondary Mentor Name

Peter Dodds

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Data Science

Primary Research Category

Engineering & Physical Sciences

Secondary Research Category

Health Sciences

Tertiary Research Category

Social Sciences

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The sentiment of depression on Twitter

Major depression is a serious health issue afflicting hundreds of millions of people each year, with many going untreated due to the intense stigma surrounding mental illness. Using the Hedonometer, we analyze the sentiment of messages containing the word ‘depression’ posted to Twitter from 2008-2018. We then use a ‘white-box’ tool developed by our group to reveal the words most responsible for sentiment shifts. In particular, we identify specific dates on which sentiment deviates largely from the baseline in response to corresponding real-world events. The results offer insight into language use associated with depression on Twitter.