Presentation Title
Measuring Mental Health Stigma on Twitter
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 Name
Peter Sheridan Dodds
Graduate Student Mentors
Thayer Alshaabi, Michael V. Arnold
Faculty/Staff Collaborators
Thayer Alshaabi, Michael V. Arnold, Peter Sheridan Dodds, Christopher M. Danforth
Status
Graduate
Student College
Graduate College
Second Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Second Program/Major
Data Science
Primary Research Category
Social Sciences
Secondary Research Category
Engineering & Physical Sciences
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.