Word use in first-person accounts of schizophrenia

Fineberg, Sarah K.; Deutsch‐Link, Sasha; Ichinose, Megan; McGuinness, Teena M; Bessette, Andrew; Chung, Cindy K.; Corlett, Philip R.

doi:10.1192/bjp.bp.113.140046

Cited by 33 publications

(35 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, Reddit posts that do not have these restrictions may reflect more natural language of users and allow for additional observations such as differences in punctuation usage. In line with the hypothesis put forth by Fineberg et al (2015) our finding that users with SZ use punctuation significantly less than CTL users may reflect more disorganized use of language, a prominent symptom of schizophrenia (Covington et al, 2005;APA, 2013).…”

Section: Discussionsupporting

confidence: 88%

Linguistic Analysis of Schizophrenia in

Zomick¹,

Levitan²,

Serper³

2019

Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

View full text Add to dashboard Cite

We explore linguistic indicators of schizophrenia in Reddit discussion forums. Schizophrenia (SZ) is a chronic mental disorder that affects a person's thoughts and behaviors. Identifying and detecting signs of SZ is difficult given that SZ is relatively uncommon, affecting approximately 1% of the US population, and people suffering with SZ often believe that they do not have the disorder. Linguistic abnormalities are a hallmark of SZ and many of the illness's symptoms are manifested through language. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ. We collected and analyzed a large corpus of Reddit posts from users claiming to have received a formal diagnosis of SZ and identified several linguistic features that differentiated these users from a control (CTL) group. We compared these results to other findings on social media linguistic analysis and SZ. We also developed a machine learning classifier to automatically identify self-identified users with SZ on Reddit.

show abstract

Section: Discussionsupporting

confidence: 88%

Linguistic Analysis of Schizophrenia in

Zomick¹,

Levitan²,

Serper³

2019

Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

View full text Add to dashboard Cite

show abstract

“…Though none of the subjects demonstrated overt distress during or after the task, we expected that they may have experienced implicit distress due the periods of volatile and untrustworthy advice in the task, or after hearing at the end that the confederate was not in fact another player, and that we had intentionally misled them. As a measure of implicit distress, we measured self-referential language (words like I, me, and mine), which has been demonstrated to increase with depression, suicidality, and other mental and physical illnesses (15, 18) (19). Control subjects did not change their use of self-referential language rom before to after the post-task disclosure, but BPD subjects used significantly fewer self-referential words after the disclosure (Figure 2C, time × group interaction F = 6.16, p = 0.02), suggesting that their distress actually decreased after it was revealed that they were interacting with a computer, rather than a human confederate.…”

Section: Resultsmentioning

confidence: 99%

“…We expected that people (and BPD > control) would experience implicit distress due the periods of volatile and untrustworthy advice in the task, or after hearing at the end that the confederate was not in fact another player, and that we had intentionally misled them. We measured implicit distress by counting self-referential language (words like I, me, and mine), as they are known to increase with distress in mental and physical illnesses (13, 14) (15).…”

Section: Introductionmentioning

confidence: 99%

Differential valuation and learning from social and non-social cues in Borderline Personality Disorder

Fineberg

Leavitt

Stahl

et al. 2018

Preprint

View full text Add to dashboard Cite

BackgroundVolatile interpersonal relationships are a core feature of Borderline Personality Disorder (BPD), and lead to devastating disruption of patients’ personal and professional lives. Quantitative models of social decision making and learning hold promise for defining the underlying mechanisms of this problem. In this study, we tested BPD and control subject weighting of social versus non-social information, and their learning about choices under stable and volatile conditions. We compared behavior using quantitative models.MethodsSubjects (n=20 BPD, n=23 control) played an extended reward learning task with a partner (confederate) that requires learning about non-social and social cue reward probability (The Social Valuation Task). Task experience was measured using language metrics: explicit emotions/beliefs, talk about the confederate, and implicit distress (using the previously established marker self-referentiality). Subjects’ weighting of social and non-social cues was tested in mixed-effects regression models. Subjects’ learning rates under stable and volatile conditions were modelled (Rescorla-Wagner approach) and group x condition interactions tested.ResultsCompared to controls, BPD subject debriefings included more mentions of the confederate and less distress language. BPD subjects also weighted social cues more heavily, but had blunted learning responses to (non-social and social) volatility.ConclusionsThis is the first report of patient behavior in the Social Valuation Task. The results suggest that BPD subjects expect higher volatility than do controls. These findings lay the groundwork for a neuro-computational dissection of social and non-social belief updating in BPD, which holds promise for the development of novel clinical interventions that more directly target pathophysiology.

show abstract

“…There is a fairly robust literature showing that increased use of first-person singular pronouns (I, me, my) is associated with increased depression or suicidality (Fineberg et al, 2015;Stirman and Pennebaker, 2001;Zimmermann et al, 2016), indicating we may expect to see a negative association between hope and this type of pronoun. Conversely, there is some literature to show use of first-person plural pronouns, such as "we," could indicate a sense of social connectedness (Tausczik and Pennebaker, 2010), giving reason to expect a positive association with this type of pronoun.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, there is some literature to show use of first-person plural pronouns, such as "we," could indicate a sense of social connectedness (Tausczik and Pennebaker, 2010), giving reason to expect a positive association with this type of pronoun. Research is less clear in guiding our expectations of associations with hope for second person (you, your) and third-person singular (she, he) and plural (they) pronouns, but studies have shown differences in the use of pronouns across categories in people with schizophrenia as compared to healthy controls and people with depression (Fineberg et al, 2015;Hong et al, 2015;Lee et al, 2007). Thus, while we had hypotheses for first-person singular and plural pronouns, analyses examining other types of pronouns were considered exploratory.…”

Section: Introductionmentioning

confidence: 99%

Language and hope in schizophrenia-spectrum disorders

Bonfils

Luther

Firmin

et al. 2016

Psychiatry Research

View full text Add to dashboard Cite

Highlights Hope is integral to recovery in schizophrenia, but its relation to speech is unknown  Lexical analysis of transcribed life narratives were examined  Total hope, agency hope, and pathways hope correlated with lexical variables  Anger words most strongly predicted total and pathways hope Abstract Hope is integral to recovery for those with schizophrenia. Considering recent advancements in the examination of clients' lexical qualities, we were interested in how clients' words reflect hope. Using computerized lexical analysis, we examined social, emotion, and future words' relations to hope and its pathways and agency components. Forty-five clients provided detailed narratives about their life and mental illness. Transcripts were analyzed using the Linguistic Inquiry and Word Count program (LIWC), which assigns words to categories (e.g., "anxiety") based on a pre-existing dictionary. Correlations and linear multiple regression were used to examine relationships between lexical qualities and hope. Hope and its subcomponents had significant or trending bivariate correlations in expected directions with several emotion-related word categories (anger and sadness) but were not associated with expected categories such as social words, positive emotions, optimism, achievement, and future words. In linear multiple regressions, no LIWC variable significantly predicted hope agency, but anger words significantly predicted both total hope and hope pathways. Our findings indicate lexical analysis tools can be used to investigate recovery-oriented concepts such as hope, and results may inform clinical practice. Future research should aim to replicate our findings in larger samples.

show abstract

Word use in first-person accounts of schizophrenia

Cited by 33 publications

References 44 publications

Linguistic Analysis of Schizophrenia in

Linguistic Analysis of Schizophrenia in

Differential valuation and learning from social and non-social cues in Borderline Personality Disorder

Language and hope in schizophrenia-spectrum disorders

Contact Info

Product

Resources

About