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Thirty-Third Annual Meeting of the Society for Research in Psychopathology

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46. Natural Language Markers Relate to Symptomatology in Individuals with Schizophrenia

Predicting impending changes in symptomatology is important in treating individuals diagnosed with schizophrenia. Natural language markers can be measured unobtrusively and might be informative about patients’ clinical needs, leading to more cost-effective and efficient care. Positive and negative symptom severity was examined at baseline and at a nine-month follow-up for 77 individuals diagnosed with schizophrenia. Natural language from both time points was evaluated using social, positive emotion, and negative emotion based lexical analysis. Participants (Mage = 35.97) were mostly male (59.7%) and Caucasian (70.1%). Cross-lagged panel modelling showed that, at baseline, negative emotion word use was significantly related to positive symptoms. None of the lexical variables were directly related to symptoms at follow-up. Increased negative emotion word use at baseline predicted increased positive symptoms nine months later, but positive symptoms at baseline did not predict negative or positive emotion word use at follow-up. Positive symptoms at baseline predicted follow-up social word use at a trend level. No significant predictive paths were found between negative symptoms and lexical variables. Individuals with schizophrenia who are using higher levels of negative emotion words may warrant extra attention, as it may suggest an impending increase in positive symptoms.

Tovah Cowan
Louisiana State University

Rachel W. Smith
Louisiana State University

Alex S. Cohen
Louisiana State University


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