Presentation Title

Measuring Mental Health Stigma on Twitter

Project Collaborators

Thayer Alshaabi, Peter Sheridan Dodds, Christopher M. Danforth

Abstract

Major depression is a serious health issue afflicting hundreds of millions of people each year, with damaging physical and emotional effects exacerbated by intense stigma. In this project, we quantify trends in stigma surrounding mental illness using Twitter data. First, we show that the phrase “mental health” increased in popularity by a factor of 10 between 2013 and 2017. Additional words and phrases occurring in these messages reveal social trends responsible for the tremendous increase in collective attention. Second, we compile a list of negative labels commonly used in stigmatizing language, finding their popularity has largely decreased during this period. Finally, we identify depression diagnosis self-disclosure statements and report on their prevalence over time.

Primary Faculty Mentor Name

Christopher M. Danforth

Secondary Mentor NetID

pdodds

Secondary Mentor Name

Peter Sheridan Dodds

Graduate Student Mentors

Thayer Alshaabi

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Data Science

Primary Research Category

Social Sciences

Second College (optional)

Honors College

Secondary Research Category

Engineering & Physical Sciences

Tertiary Research Category

Health Sciences

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Measuring Mental Health Stigma on Twitter

Major depression is a serious health issue afflicting hundreds of millions of people each year, with damaging physical and emotional effects exacerbated by intense stigma. In this project, we quantify trends in stigma surrounding mental illness using Twitter data. First, we show that the phrase “mental health” increased in popularity by a factor of 10 between 2013 and 2017. Additional words and phrases occurring in these messages reveal social trends responsible for the tremendous increase in collective attention. Second, we compile a list of negative labels commonly used in stigmatizing language, finding their popularity has largely decreased during this period. Finally, we identify depression diagnosis self-disclosure statements and report on their prevalence over time.