Quantifying the Language of Schizophrenia in Social Media
Open accessAbstract
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.
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Automated acoustic metrics can be extracted from speech samples that are not labeled.
“Automated acoustic metrics can be extracted from non-labeled speech samples [35], [39], [38], [102].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [38]
Participants with AD show higher proportions of voiceless speech and lower proportions of voiced speech, indicating reduced fluency and shorter fluent segments.
“First, the spontaneous speech analysis indicated that participants with AD exhibited higher proportions of voiceless speech and lower proportions of voiced speech, indicating a loss of fluency and shortening of fluent speech segments for those with AD.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [38]
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- Authors
- Mitchell, M., Hollingshead, K., & Coppersmith, G.
- Affiliations
- Florida Institute for Human and Machine Cognition (US)
- Venue
- Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality
- Published
- 2015
- Pages
- 11-20
- OpenAlex
- W2252128283
- Cited by
- 172
- References
- 29
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 14 · 2023: 21 · 2022: 16 · 2021: 27 · 2020: 17 · 2019: 17 · 2018: 19 · 2017: 14 · 2016: 11 · 2015: 4
- FWCI
- 28.09
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Computational Linguistics and Clinical Psychology (CLPsych) (publishedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- schizophrenia, linguistic markers, social media language, Twitter, LIWC, natural language processing, mental health, symptom detection, help-seeking
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