How does Bipolar and Depressive Diagnoses Reflect in Linguistic Usage on Twitter : A Study using LIWC and Other Tools

Abstract: Depression and bipolar disorder are two mental disorders which left untreated can have a devastating effect on a persons life as they are considered both chronic and disabling. Seeking help is often a big step that can be procrastinated for years, and misdiagnosis is a very common problem once contact with psychiatric care has finally been established. This paper investigates the correlation between posting patterns on Twitter and suffering from these diagnoses. For each day of the past year we quantify cues for emotional intensity and polarity, involvement with their social network and activity as well as metrics previously shown to be associated with depression. A number of statistical tests, including Anova, t-testing and Covariance analysis, are then constructed and fitted over our data. Our results show a significant difference between our groups in affective language use tied to emotional polarity as well as an elevated use of first person personal pronouns for both the depressed and bipolar group. These findings indicate strongly that our approach is valid for finding cues about mental illness, however the strong limitations in our data collections approach needs to be addressed in order for our results to have real scientific merit. This study is motivated by the need for finding predictive models for mental disorders, and to better understand the disorders themselves. Predictive models can be helpful for proper diagnosis by a clinical psychologist as well as for helping more people seek treatment.