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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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Abstract:
The menstrual cycle affects many aspects of female physiology, from the immune system to behavioral and emotional regulation. It is unclear however if these physiological changes are reflected in everyday, naturalistic language production, and moreover whether these putative effects can be consistently quantified. Using a novel approach based on social networks, we characterized linguistic expression differences in female and male volunteers over the course of several months, while having no physiological or reported information of the female participants' menstrual cycles. We used a simple algorithm to quantify the linguistic affect intensity of 418 (184 females and 234 males) subjects using their social networks production and found a 7-day modulatory cycle of affect intensity that corresponds to labor-week fluctuations, with no significant difference by biological sex, and a 28-day cycle over which females are significantly different than males. Our results are consistent with the hypothesis that the menstrual cycle modulates affective features of naturalistic linguistic production.
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Keyword:
Neuroscience
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389828/ http://www.ncbi.nlm.nih.gov/pubmed/30837849 https://doi.org/10.3389/fnint.2019.00005
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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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The ontogeny of discourse structure mimics the development of literature ...
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