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Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study
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In: Journal of Medical Internet Research (2020)
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Using language processing and speech analysis for the identification of psychosis and other disorders
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In: Biol Psychiatry Cogn Neurosci Neuroimaging (2020)
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Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion
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In: PLoS One (2020)
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Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook
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In: NPJ Schizophr (2020)
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Language as a Biomarker for Psychosis: A Natural Language Processing Approach
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In: Schizophr Res (2020)
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Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study
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In: J Med Internet Res (2020)
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Linguistic markers predict onset of Alzheimer's disease
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In: EClinicalMedicine (2020)
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Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing
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From discourse to pathology: Automatic identification of Parkinson’s disease patients via morphological measures across three languages
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In: Cortex (2020)
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The history of writing reflects the effects of education on discourse structure: implications for literacy, orality, psychosis and the axial age
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Differential 28-Days Cyclic Modulation of Affective Intensity in Female and Male Participants via Social Media
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S19. ANALYZING NEGATIVE SYMPTOMS AND LANGUAGE IN YOUTHS AT RISK FOR PSYCHOSIS USING AUTOMATED LANGUAGE ANALYSIS
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24.2 NATURAL LANGUAGE PROCESSING STUDIES OF PSYCHOSIS AND ITS RISK STATES
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Prediction of psychosis across protocols and risk cohorts using automated language analysis
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The maturation of speech structure in psychosis is resistant to formal education
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Predicting natural language descriptions of mono-molecular odorants
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The maturation of speech structure in psychosis is resistant to formal education
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Self-reference in psychosis and depression: a language marker of illness
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Fineberg, Sarah Kathryn; Leavitt, Jacob D.; Deutsch-Link, Sasha; Dealy, Samson; Landry, Christopher D.; Pirruccio, Kevin; Shea, Samantha; Trent, Savannah; Cecchi, Guillermo; Corlett, Philip R.
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In: Psychol Med (2016)
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Abstract:
BACKGROUND: Language use is of increasing interest in the study of mental illness. Analytical approaches range from phenomenological and qualitative through formal computational quantitative methods. Practically, the approach may have utility in predicting clinical outcomes. We harnessed a real-world sample (blog entries) from groups with psychosis, strong beliefs, odd beliefs, illness, mental illness, and/or social isolation to validate and extend laboratory findings about lexical differences between psychosis and control subjects. METHODS: We describe the results of two experiments using Linguistic Inquiry and Word Count (LIWC) software to assess word category frequencies. In experiment 1, we compared word use in psychosis and control subjects in the laboratory (23/group), and related results to subject symptoms. In experiment 2, we examined lexical patterns in blog entries written by people with psychosis and 8 comparison groups. In addition to between-group comparisons, we used factor analysis followed by clustering to discern the contributions of strong belief, odd belief, and illness identity to lexical patterns. FINDINGS: Consistent with others’ work, we found that first person pronouns, biological process words, and negative emotion words were more frequent in psychosis language., We tested lexical differences between bloggers with psychosis and multiple relevant comparison groups. Clustering analysis revealed that word use frequencies did not group individuals with strong or odd beliefs, but instead grouped individuals with any illness (mental or physical). INTERPRETATION: Pairing of laboratory and real-world samples reveals that lexical markers previously identified as specific language changes in depression and psychosis are likely markers of illness in general.
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Keyword:
Article
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URL: http://www.ncbi.nlm.nih.gov/pubmed/27353541 https://doi.org/10.1017/S0033291716001215 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944937/
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