Analyzing symptoms of schizophrenia has traditionally been challenging given the low prevalence of the condition, affecting around 1% of the U.S. population. We explore potential linguistic markers of schizophrenia using the tweets 1 of self-identified schizophrenia sufferers, and describe several natural language processing (NLP) methods to analyze the language of schizophrenia. We examine how these signals compare with the widelyused LIWC categories for understanding mental health (Pennebaker et al., 2007) , and provide preliminary evidence of additional linguistic signals that may aid in identifying and getting help to people suffering from schizophrenia.
REF studied self-identified schizophrenia patients on Twitter and found that linguistic signals may aid in identifying and getting help to people suffering from it.
Quantifying the Language of Schizophrenia in Social Media
CLPsych@HLT-NAACL
Computer Science