Linguistic Analysis of Schizophrenia in

Abstract

We explore linguistic indicators of schizophrenia in Reddit discussion forums. Schizophrenia (SZ) is a chronic mental disorder that affects a person's thoughts and behaviors. Identifying and detecting signs of SZ is difficult given that SZ is relatively uncommon, affecting approximately 1% of the US population, and people suffering with SZ often believe that they do not have the disorder. Linguistic abnormalities are a hallmark of SZ and many of the illness's symptoms are manifested through language. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ. We collected and analyzed a large corpus of Reddit posts from users claiming to have received a formal diagnosis of SZ and identified several linguistic features that differentiated these users from a control (CTL) group. We compared these results to other findings on social media linguistic analysis and SZ. We also developed a machine learning classifier to automatically identify self-identified users with SZ on Reddit.

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

“…These limitations explain why most of the work reported has focused on Twitter, which remains open for data analysis. Nonetheless, other social networks have been shown to be useful for biomedicine, including Facebook (12), Instagram (3), Reddit (45)(46)(47), and even YouTube (48,49).…”

“…By "social media" we mean any user-generated content, including posts to sites such as Twitter and Facebook but also comments on disease-specific or health-related sites, forums, or chats. Most social media sites have been shown to be relevant for biomedical studies, including Twitter (15), Facebook (12), Flickr (17), Instagram (3), Reddit (45)(46)(47), and even Youtube (48,49). Used together with other sources of data such as web search, mobility data, scientific publications, EHRs, and genome-wide studies, social media data help researchers build population-and individual-level observation tools that can speed translational research in fundamentally new ways.…”

Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts

“…Research over the past decade has supported and validated the use of computational linguistics techniques applied to social media data for predicting and detecting mental illness across a broad range of psychiatric conditions (Guntuku et al, 2017;Wongkoblap et al, 2017). To date, linguistic indicators of psychopathology have been identified for a wide range of psychiatric conditions (Zomick et al, 2019;Coppersmith et al, 2015;Birnbaum et al, 2017;Huang et al, 2017;De Choudhury et al, 2013;Shen and Rudzicz, 2017). Recent work has also looked at detecting and predicting suicidality using linguistic features from social media posts (Du et al, 2018;Coppersmith et al, 2018;Zirikly et al, 2019).…”

Detection of Mental Health from Reddit via Deep Contextualized Representations

“…However, it is often unclear whether the findings are specific to the disorder, or if they are indicative of mental disorders more generally. For example, in pairwise analyses, personal pronoun usage has been found to be increased in individuals with schizophrenia (Zomick et al, 2019). However, this pattern might or might not be specific to schizophrenia, but may be indicative of other mental disorders as well.…”

“…Using computational linguistics, researchers have been able to leverage the widespread use of social media to analyze large, publicly available datasets for identifying linguistic markers of mental illness. To date, unique linguistic markers and patterns have been identified for several psychiatric conditions, such as major depressive disorder (MDD) (De Choudhury et al, 2013;Vedula and Parthasarathy, 2017), general anxiety disorder (GAD) (Shen and Rudzicz, 2017), bipolar disorder (BD) (Huang et al, 2017;Sekulić et al, 2018), eating disorders (ED) (Mohammadi et al, 2019;Naderi et al, 2019), schizophrenia (SZ) (Mitchell et al, 2015;Birnbaum et al, 2017;Zomick et al, 2019), obsessive compulsive disorder (OCD) (Coppersmith et al, 2015a), posttraumatic stress disorder (PTSD) (Coppersmith et al, 2014), as well as others (Coppersmith et al, 2015a). Linguistic findings have spanned various domains of language, including the use of pronouns, emotion words, tentative language, tangentiality, punctuation, and content analysis.…”