Mining Twitter Data to Improve Detection of Schizophrenia

Kimberly McManus, BS,*,1 Emily K. Mallory, BS,*,1 Rachel L. Goldfeder, MS,*,1 Winston A. Haynes, BA,*,1 and Jonathan D. Tatum, BS*,1

Go to:

Abstract

Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models.