Presentation Title

Measuring Mental Health Stigma on Twitter

Project Collaborators

Thayer Alshaabi, Michael V. Arnold, Peter Sheridan Dodds, Christopher M. Danforth

Abstract

Serious mental health problems afflict hundreds of millions of people each year, with many going untreated due to the intense stigma surrounding mental illness. In the present study, we explore trends in words and phrases related to mental health through a collection of 1- , 2-, and 3-grams parsed from a data stream of roughly 10% of all English tweets since 2012. We examine temporal dynamics of mental health language, finding that the usage rank of the phrase ‘mental health’ has increased by an order of magnitude since 2013. We also investigate the ambient sentiment of tweets containing the phrase ‘mental health’, highlighting specific dates where sentiment deviates substantially from the baseline. Finally, we use the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language due to social amplification. Since 2015, mentions of mental health related phrases are increasingly due to retweets, suggesting that stigma associated with discussion of mental health on Twitter has diminished with time.

Primary Faculty Mentor Name

Christopher M. Danforth

Secondary Mentor NetID

pdodds

Secondary Mentor Name

Peter Sheridan Dodds

Graduate Student Mentors

Thayer Alshaabi, Michael V. Arnold

Status

Graduate

Student College

Graduate College

Program/Major

Complex Systems

Primary Research Category

Social Sciences

Second College (optional)

College of Engineering and Mathematical Sciences

Second Program/Major

Data Science

Secondary Research Category

Engineering & Physical Sciences

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Measuring Mental Health Stigma on Twitter

Serious mental health problems afflict hundreds of millions of people each year, with many going untreated due to the intense stigma surrounding mental illness. In the present study, we explore trends in words and phrases related to mental health through a collection of 1- , 2-, and 3-grams parsed from a data stream of roughly 10% of all English tweets since 2012. We examine temporal dynamics of mental health language, finding that the usage rank of the phrase ‘mental health’ has increased by an order of magnitude since 2013. We also investigate the ambient sentiment of tweets containing the phrase ‘mental health’, highlighting specific dates where sentiment deviates substantially from the baseline. Finally, we use the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language due to social amplification. Since 2015, mentions of mental health related phrases are increasingly due to retweets, suggesting that stigma associated with discussion of mental health on Twitter has diminished with time.