Semantic Characteristics of Schizophrenic Speech Kfir Bar∗ Vered Zilberstein∗;∗∗ Ido Ziv∗ School of Computer Science School of Computer Science Department of Psychology College of Management Tel Aviv University College of Management Academic Studies Ramat Aviv, Israel Academic Studies Rishon LeZion, Israel [email protected] Rishon LeZion, Israel [email protected] [email protected] Heli Baram∗ Nachum Dershowitz Department of Psychology School of Computer Science Ruppin Academic Center Tel Aviv University Emek Hefer, Israel Ramat Aviv, Israel [email protected] [email protected] Samuel Itzikowitz Eiran Vadim Harel School of Computer Science Beer Yaakov Mental Heath Center College of Management Academic Studies Beer Yaakov, Israel Rishon LeZion, Israel [email protected] [email protected] Abstract of creativity. Bleuler focused mostly on “loosen- ing of associations”, or derailment, a thought dis- Natural language processing tools are used order characterized by the usage of unrelated con- to automatically detect disturbances in tran- cepts in a conversation, or in other words, a con- scribed speech of schizophrenia inpatients versation lacking coherence. The Diagnostic and who speak Hebrew. We measure topic muta- tion over time and show that controls main- Statistical Manual of Mental Disorders (DSM 5) tain more cohesive speech than inpatients. We (Association, 2013) outlines disorganized speech also examine differences in how inpatients and as one of the criteria for making a diagnosis of controls use adjectives and adverbs to describe schizophrenia. Morice and Ingram (1982) showed content words and show that the ones used that schizophrenics’ speech is built upon a dif- by controls are more common than the those ferent syntactic structure than normal controls, of inpatients. We provide experimental re- and that this difference increases over time. An- sults and show their potential for automatically detecting schizophrenia in patients by means dreasen (1979) suggested several definitions of only of their speech patterns. linguistic and cognitive behaviors frequently ob- served in patients, and which may be useful for 1 Introduction thought-disorder evaluation. Among the defini- tions presented in that report, one finds the follow- arXiv:1904.07953v1 [cs.CL] 16 Apr 2019 Thought disorders are described as disturbances in ing, which we address in this study: the normal way of thinking. Bleuler (1991) orig- inal considered thought disorders to be a speech Incoherence, also known as “word salad”, refers impairment in schizophrenia patients, but nowa- to speech that is incomprehensible at times due to days there is agreement that thought disorders are multiple grammatical and semantic inaccuracies. also relevant to other clinical disorders, including In this paper, we focus mostly on the semantic in- pediatric neurobehavioral disorders like attention accuracies, leaving grammatical issues for future deficit hyperactivity disorder and high functioning investigation. autism. They can even occur in normal popula- Derailment, also known as “loose associations”, tions, especially in people who have a high level happens when a speaker shifts among topics that ∗∗Equal contribution. are only remotely related, or are completely unre- ∗∗∗∗Supported by the Deutsch Institute. lated, to the previous ones. Tangentiality occurs when an irrelevant, or just recorded interviews, which achieves 81.5% barely relevant, answer is provided for a given accuracy. question. We focus here on derailment. But tangentiality We proceed as follows: The next section re- has been addressed in some other studies. The two views some relevant previous work. In Section3, notions are closely related. we describe how we collected the data. Our main One of the main data sources for diagnosing contributions are described in Section4, followed mental disorders is speech, typically collected dur- by some conclusions suggested in the final section. ing a psychiatric interview. Identifying signals 2 Related Work that indicate the presence of thought disorders is often challenging and subjective, especially in pa- There is a large body of work that examines tients who are not undergoing a psychotic episode human-generated texts with the aim of learning at the time of the interview. about the way people who suffer from various In this work, we focus on schizophrenia. We mental-health disorders use language in different investigate a number of semantic characteristics settings. For example, Al-Mosaiwi and Johnstone of transcribed human speech, and propose a way (2018) conducted a study in which they analyzed to use them to measure disorganized speech. 63 web forums, some related to mental health dis- Natural-language processing software is used to orders and others used as control. They ran their automatically detect those characteristics, and we analysis with the well-known Linguistic Inquiry suggest a way of aggregating them in a meaning- and Word Count (Pennebaker et al., 2015) tool to ful way. We use transcribed interviews, collected find absolutist words in free text. Overall, they from Hebrew-speaking schizophrenia inpatients at discovered that anxiety, depression, and suicidal- a mental health hospital and from a control group. ideation forums contained more absolutist words About two thirds of the patients were identified as than control forums. in schizophrenia remission at the time of the inter- Recently, social media have become a vital view. source for learning about how people who suf- Following a few previous works (Iter et al., fer from mental-health disorders use language. 2018; Bedi et al., 2015), we measure Andreasen’s Several studies collect relevant users from Twit- derailment by calculating average semantic sim- ter,1 by considering users who intentionally write ilarity between consecutive chunks of a running about their diagnosed mental-health disorders. For text to track topical mutations, and show the differ- example, in (De Choudhury et al., 2013; Tsug- ence between patients and controls. For incoher- awa et al., 2015), some language characteristics ence, we look at word modifiers, focusing on ad- of Twitter users who claim to suffer from a clin- jectives and adverbs, that subjects use to describe ical depression are studied. Similarly, users who the same objects, and then learn the difference be- suffer from post traumatic stress disorder are ad- tween the two groups. As a final step, we use those dressed in (Coppersmith et al., 2014). Mitchell et semantic characteristics in a classification setting al. (2015) analyze tweets posted by schizophren- and argue for their usability. ics, and Coppersmith et al. (2016) investigate the This work makes the following contributions: language and emotions that are expressed by users who have previously attempted to commit suicide. • We measure derailment in speech using word Coppersmith et al. (2015) work with users who semantics, similar to (Bedi et al., 2015), this suffer from a broad range of mental-health con- time on Hebrew. ditions and explore language differences between groups. Most of these works found a significant • We explore a novel way of measuring one difference in the usage of some linguistic charac- aspect of speech incoherence, by measur- teristics by the experience group when compared ing how similar modifiers (adjectives and ad- to a control group. Furthermore, different levels of verbs) are to ones used in a reference text to these linguistic characteristics are used as features describe the same words. for training a classifier to detect mental-health dis- orders prior to the report date. • Using these measures, we build a classifier for detecting schizophrenia on the basis of 1https://twitter.com Reddit2 has also been identified as a convenient do not have a good semantic representation. Iter source for collecting data for this goal. Losada et al. (2018) addressed this suggestion by clean- and Crestani (2016) outline a methodology for col- ing the patients’ responses of all those words and lecting posts and comments of Reddit and Twit- expressions (e.g. uh, um, you know) prior to cal- ter users who suffer from depression. Similarly, culating the semantic scores. This gave a slight a large dataset of Reddit users with depression, improvement, although measured over a relatively manually verified (by lay annotators for an explicit small set of participants. Instead of working with claim of diagnosis), has been released for pub- chunks of text, they worked with full sentences, lic use (Yates et al., 2017). In that work, the au- and replaced LSA with some modern techniques thors employ a deep neural network on the raw text for sentence embeddings. Likewise, in our work, for detecting clinically depressed people ahead of we use word embeddings instead of LSA. time, achieving 65% F1 score on an evaluation set. Bedi et al. (2015) define coherence as an ag- A few caveats are in order when using so- gregation of the cosine similarity between pairs cial media for analyzing mental health conditions. of consecutive sentences, each represented by First, self reporting of a mental health disorder is the element-wise average vector of the individ- not a popular course of action. Clearly, then, the ual words’ LSA vectors. They worked with a experimental group is chosen from a subgroup of group of 34 youths at clinical high-risk for psy- the relevant population. Second, the controls, typ- chosis, interviewed them quarterly for 2 1/2 years, ically collected randomly “from the wild”, are not and transcribed their answers. Five out of the 34 guaranteed to be free of mental-health disorders. transitioned to psychosis. They used coherence Finally, social media posts are considered to be scores, along with part-of-speech information, to a different form of communication than ordinary automatically predict transition to psychosis with speech. For all these reasons, in this work, we 100% accuracy. use validated experimental and control groups in The goal of all these works, including ours, is an interview setting. to automatically detect disorganized speech in a Measuring various aspects of incoherence in more objective and reliable way.