Proceedings of the Fourth Workshop on Computational Linguistics and
          Clinical Psychology –- From Linguistic Signal to Clinical Reality

Hollingshead, Kristy; Ireland, Mark E.; Loveys, Kate

doi:10.18653/v1/w17-31

Cited by 7 publications

(6 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consistent with the first hypothesis (Hypothesis 1a), the results indicated that concurrent session-level distress was associated with clients’ linguistic features, such that when clients experienced less distress prior to a session they used fewer first-person singular and negative emotion words, and more positive emotion words in that session. These findings are in line with theoretical views highlighting the role of enhanced self-focus and negative emotions and the lack of positive emotions among individuals who tend to experience high levels of distress (e.g., Blatt, 1995; Shahar et al, 2020) and echo recent reviews (Morales et al, 2017). Unlike previous studies that have mostly been cross-sectional, the current study charted within-client associations between these linguistic features and levels of distress session-by-session across treatment.…”

Section: Discussionsupporting

confidence: 90%

“…Hypothesis 2: At the between client level, fewer first-person singular and negative emotion words, and more positive emotion words will be associated with lower levels of distress, both across treatment (2a) as well as at the initial phase of treatment (2b). These hypotheses are consistent with previous studies that indicated associations between these linguistic features and levels of distress at the between-person level (Morales et al, 2017). Because most studies that have described these associations were based on much larger samples (e.g., Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015; Tsugawa et al, 2015) than the number of clients in the current study, 1 these hypotheses will be tested in an exploratory manner.…”

supporting

confidence: 93%

“…A growing empirical literature, mostly from outside the field of psychotherapy, shows that systematic measures of language are associated with the level of experienced distress (for a review, see Guntuku, Yaden, Kern, Ungar, & Eichstaedt, 2017; Morales, Scherer, & Levitan, 2017; Tausczik & Pennebaker, 2010). For example, the use of first-person singular was found to be associated with distress severity by Weintraub (1981).…”

mentioning

confidence: 99%

“…According to recent literature reviews, the most common features repeatedly found across studies to be associated with higher levels of distress are increased use of the first-person singular, increased use of negatively valenced words and decreased use of positively valenced words (Edwards & Holtzman, 2017; Guntuku et al, 2017; Morales et al, 2017; Tausczik & Pennebaker, 2010). These findings are in line with the theoretical literature that highlights the dominant role of self-focus and self-criticism in maintaining and intensifying individuals’ negative affect, which in turn leads to increased symptoms of distress (Beck, 1967; Blatt, 1995; Pyszczynski & Greenberg, 1987; Shahar, Rogers, Shalev, & Joiner, 2020).…”

mentioning

confidence: 99%

“…We explored similar prospective associations, that is, between the linguistic features and the next-session distress (Hypothesis 1b). These predictions were based on recent reviews reporting an association between these features and distress level in cross-sectional studies (e.g., Guntuku et al, 2017; Morales et al, 2017) and psychotherapy studies showing associations between linguistic features and session outcomes (e.g., Kraemer et al, 2007).…”

mentioning

confidence: 99%

See 4 more Smart Citations

Using computerized text analysis to examine associations between linguistic features and clients’ distress during psychotherapy.

Shapira¹,

Lazarus²,

Goldberg³

et al. 2021

Journal of Counseling Psychology

View full text Add to dashboard Cite

Raw linguistic data within psychotherapy sessions may provide important information about clients' progress and well-being. In the current study, computerized text analytic techniques were applied to examine whether linguistic features were associated with clients' experiences of distress within and between clients and whether changes in linguistic features were associated with changes in treatment outcome. Transcripts of 729 psychotherapy sessions from 58 clients treated by 52 therapists were analyzed. Prior to each session, clients reported their distress level. Linguistic features were extracted automatically by using natural language parser for first-person singular identification and using positive and negative emotion words lexicon. The association between linguistic features and levels of distress was examined using multilevel models. At the within-client level, fewer first-person singular words, fewer negative emotional words and more positive emotional words were associated with lower distress in the same session; and fewer negative emotion words were associated with lower next session distress (rather small f 2 effect sizes ϭ 0.011 Ͻ f 2 Ͻ 0.022). At the between-client level, only first session use of positive emotion words was associated with first session distress ( p 2 effect size ϭ 0.08). A drop in the use of first-person singular words was associated with improved outcome from pre-to posttreatment (small p 2 effect size ϭ 0.05). The findings provide preliminary support for the association between clients' linguistic features and their fluctuating experience of distress. They point to the potential value of computerized linguistic measures to track therapeutic outcomes. Public Significance StatementThe current study explored whether utterances within sessions contain information about a client's progress. We used automated text analytic techniques to examine whether clients' linguistic features were associated with their experience of distress both relative to themselves and relative to others, and whether changes in these linguistic features were associated with treatment outcomes. The results indicated that when clients experienced less of distress in the days prior to a session they tended to use fewer first-person singular words, more positive emotion words and fewer negative emotion words in that session. In addition, when clients used fewer negative emotion words in a session, their distress in the days prior to the next session tended to be lower. Lastly, lesser use of first-person singular words in the later versus earlier stages of therapy was associated with improved treatment outcomes. These findings provide preliminary support for the potential use of raw linguistic data within psychotherapy session as a complement to standard monitoring systems to evaluate clients' progress.

show abstract

Section: Discussionsupporting

confidence: 90%

supporting

confidence: 93%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Using computerized text analysis to examine associations between linguistic features and clients’ distress during psychotherapy.

Shapira¹,

Lazarus²,

Goldberg³

et al. 2021

Journal of Counseling Psychology

View full text Add to dashboard Cite

show abstract

Multitask Learning for Mental Health Conditions with Limited Social Media Data

Benton¹,

Mitchell²,

Hovy³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

150

135

View full text Add to dashboard Cite

Language contains information about the author's demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.

show abstract

A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

Jagfeld

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

View full text Add to dashboard Cite

Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery -living a satisfying and contributing life along symptoms of severe mental health issues -so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse firstperson accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.

show abstract

Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality

Cited by 7 publications

References 53 publications

Using computerized text analysis to examine associations between linguistic features and clients’ distress during psychotherapy.

Using computerized text analysis to examine associations between linguistic features and clients’ distress during psychotherapy.

Multitask Learning for Mental Health Conditions with Limited Social Media Data

A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

Contact Info

Product

Resources

About