A Quantitative and Qualitative Analysis of Schizophrenia Language (2022)

Abstract: Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families. Patients with schizophrenia suffer different symptoms: formal thought disorder (FTD), delusions, and emotional flatness. In this paper, we quantitatively and qualitatively analyze the language of patients with schizophrenia measuring various linguistic features in two modalities: speech and written text. We examine the following features: coherence and cohesion of thoughts, emotions, specificity, level of commit- ted belief (LCB), and personality traits. Our results show that patients with schizophrenia score high in fear and neuroticism compared to healthy controls. In addition, they are more committed to their beliefs, and their writing lacks details. They score lower in most of the linguistic features of cohesion with significant p-values.

## introduction schizophrenia is a mental illness that can disrupt thought processes and perception @xcite . it can impair people's ability to manage their emotions, and can cause motor and behavioral disorders @xcite . understanding and identifying the underlying signs of schizophrenia is critical in early detection and intervention before the malady becomes severely disabling if left untreated @xcite . moreover, it is vital to support mental health practitioners as well as policymakers to eliminate barriers to treating mental illnesses such as schizophrenia. gradual decline in functioning and cognition are some common characteristics of schizophrenia patients. symptoms may include delusions, which are fixed false beliefs, as well as hallucinations but also importantly, they tend to have strong convictions regardless of the veridicality of the beliefs themselves. another symptom that some individuals with schizophrenia exhibit is formal thought disorder (ftd), where a patient becomes unable to form coherent or logical thoughts @xcite . moreover, they suffer in some cases from lack of motivation and/or emotional response. one way to capture mental disorders and related symptomatology is by analyzing patients' linguistic cues. hence, we map the aforementioned symptoms to linguistic features that we can measure. to date, most of the employed measures used by clinicians measure superficial linguistic cues and they tend to be more qualitative. we hypothesize that advances in pragmatic nlp tools allow us to measure many of these symptoms via analyzing language cues used by patients. we surmise that given such tools, we help create objective quantitative measures for clinicians beyond what they are using today for diagnostics. moreover having such tools could help them discover and codify further studies allowing for even more signals in detecting such mental health disorders. 1 accordingly, we present the first comprehensive study of deep pragmatically oriented linguistic modeling tools for diagnostic purposes. we leverage an emotion detection model to assess the lack of emotional response. we also employ a personality detection model to measure lack of motivation, which is one of the negative symptoms they may exhibit. we use a level of committed belief detection model to identify the level of committed belief corresponding to strength of conviction. formal thought disorder (ftd) is measured by using language model-based sentence scoring as well as other coherence features such as lsa, connectives, lexical diversity, syntactic complexity, word information, and level of linguistic specificity. finally, we employ the coh-metrix computational tool for analyzing texts for a variety of cohesion measures. @xcite . accordingly, we investigate the following metrics: cohesion, level of committed belief, emotion, and personality and their corresponding correlation with symptomatic patients' language use. we examine both speech and text modalities, comparing patients vs. a matched set of controls. our results show that when patients express their emotions in writing or speech, they tend to show fear more often than other emotions. the findings also detect a neuroticism personality as they may suffer from feelings such as anger, and anxiety more frequently and severely. furthermore, the results indicate that their writings lack specificity (details), and they are more committed to their beliefs in contrast with healthy controls. in addition, our results show that writings of healthy controls are more coherent demonstrated via the high scores of language model probabilities of their writing. to the best of our knowledge, our findings present the first set of measurable pragmatic linguistic cues that significantly correlate with contrastive mental health patients' language use that goes beyond the typical superficial metrics used in the literature to date. our study provides a set of objective linguistic measures that can serve as metrics that further assist clinicians and policy makers in the mental health domain. the contributions of this paper are as follows: ## related work language provides significant insight into the content of thought. it also reflects the presence of impairments resulting from mental disorders such as schizophrenia. the predominent reflection of mental impairment for schizophrena is the lack of coherent text or speech. accordingly, cohesion scores were first proposed as an indicator of predicting schizophrenia @xcite where they used latent semantic analysis (lsa) as a feature extractor. this was further amplified by @xcite where they measured the semantic coherence in disorganized speech captured by lsa, specifically where large amounts of language overlap was interpreted as coherent language. the study found that these features, together with syntactic markers of complexity, could predict later development of psychosis with 100% accuracy using a convex hull algorithm. later, corcoran et al. different from previous studies of schizophrenia, we propose measuring cohesion using language model perplexity. moreover, we provide a comprehensive exploration of the language of patients relative to that of controls along the following linguistics cues: coherence, emotion, personality, level of specificity, and level of committed belief. ## data our study comprises two datasets speech, lab-speech, and written text, labwriting. the data is obtained from schizophrenia patients and healthy controls. both datasets are described in detail in @xcite . 2 labwriting has 188 participants who are native english-speakers between the ages of 18 -50 years, corresponding to 93 patients and 95 healthy controls. all participants are asked to write two paragraph-long essays: the first one is about their average sunday and the second essay is about what makes them the angriest. the total number of writing samples collected from both patients and controls is 373 pieces of text. the second dataset, labspeech, includes three questions that prompt participants to describe some emotional and social events. patients and controls are asked to describe (1) a picture, (2) their ideal day, and (3) their scariest experience. the total number of speech script samples collected from both patients and controls is 431. speech data is transcribed to text and a punctuation tool @xcite is used to add the missing punctuation. ## conclusion patients with schizophrenia experience different symptoms, some of which involve problems with concentration and memory, which in return may lead to disorganization in speech or behavior. therefore, diagnosing this disorder early and correctly is extremely important as it may help alleviate the adverse effects on patients. among the linguistic features of cohesion investigated in this study, we found that patients' scores are lower, with significant p-values in information structure (givenness), lexical diversity except for type-token ratio (ttr), connectives, and syntactic complexity in both datasets. among the pragmatic cues, we found that patients' score high in fear, and their personality is associated with elevated neuroticism. they also show more commitment to their beliefs, and their average specificity at sentence and word levels is lower than controls. in the future, we plan to expand our analysis to other related mental health disorders. we also plan to explore the pragmatically motivated linguistics features of schizophrenia in other languages.