Literature DB >> 29504797

Depression, negative emotionality, and self-referential language: A multi-lab, multi-measure, and multi-language-task research synthesis.

Allison M Tackman1, David A Sbarra1, Angela L Carey1, M Brent Donnellan2, Andrea B Horn3, Nicholas S Holtzman4, To'Meisha S Edwards4, James W Pennebaker5, Matthias R Mehl1.   

Abstract

Depressive symptomatology is manifested in greater first-person singular pronoun use (i.e., I-talk), but when and for whom this effect is most apparent, and the extent to which it is specific to depression or part of a broader association between negative emotionality and I-talk, remains unclear. Using pooled data from N = 4,754 participants from 6 labs across 2 countries, we examined, in a preregistered analysis, how the depression-I-talk effect varied by (a) first-person singular pronoun type (i.e., subjective, objective, and possessive), (b) the communication context in which language was generated (i.e., personal, momentary thought, identity-related, and impersonal), and (c) gender. Overall, there was a small but reliable positive correlation between depression and I-talk (r = .10, 95% CI [.07, .13]). The effect was present for all first-person singular pronouns except the possessive type, in all communication contexts except the impersonal one, and for both females and males with little evidence of gender differences. Importantly, a similar pattern of results emerged for negative emotionality. Further, the depression-I-talk effect was substantially reduced when controlled for negative emotionality but this was not the case when the negative emotionality-I-talk effect was controlled for depression. These results suggest that the robust empirical link between depression and I-talk largely reflects a broader association between negative emotionality and I-talk. Self-referential language using first-person singular pronouns may therefore be better construed as a linguistic marker of general distress proneness or negative emotionality rather than as a specific marker of depression. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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Year:  2018        PMID: 29504797     DOI: 10.1037/pspp0000187

Source DB:  PubMed          Journal:  J Pers Soc Psychol        ISSN: 0022-3514


  21 in total

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Authors:  Hejun Gu; Weiran Yan; Ehsan Elahi; Yuxia Cao
Journal:  Environ Sci Pollut Res Int       Date:  2019-11-26       Impact factor: 4.223

2.  Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup.

Authors:  Sarah Seraj; Kate G Blackburn; James W Pennebaker
Journal:  Proc Natl Acad Sci U S A       Date:  2021-02-16       Impact factor: 11.205

3.  Individuals with depression express more distorted thinking on social media.

Authors:  Krishna C Bathina; Marijn Ten Thij; Lorenzo Lorenzo-Luaces; Lauren A Rutter; Johan Bollen
Journal:  Nat Hum Behav       Date:  2021-02-11

4.  Contact with an Ex-partner is Associated with Psychological Distress after Marital Separation.

Authors:  Karey L O'Hara; Austin M Grinberg; Allison M Tackman; Matthias R Mehl; David A Sbarra
Journal:  Clin Psychol Sci       Date:  2020-05-04

5.  The relationship between text message sentiment and self-reported depression.

Authors:  Tony Liu; Jonah Meyerhoff; Johannes C Eichstaedt; Chris J Karr; Susan M Kaiser; Konrad P Kording; David C Mohr; Lyle H Ungar
Journal:  J Affect Disord       Date:  2021-12-25       Impact factor: 4.839

6.  Two is better than one: Using a single emotion lexicon can lead to unreliable conclusions.

Authors:  Gabriela Czarnek; David Stillwell
Journal:  PLoS One       Date:  2022-10-14       Impact factor: 3.752

7.  DNA Methylation Across the Serotonin Transporter Gene Following Marital Separation: A Pilot Study.

Authors:  David A Sbarra; Chelsea C Cook; Karen Hasselmo; Muhammad S Noon; Matthias R Mehl
Journal:  Ann Behav Med       Date:  2019-11-09

8.  Facebook language predicts depression in medical records.

Authors:  Johannes C Eichstaedt; Robert J Smith; Raina M Merchant; Lyle H Ungar; Patrick Crutchley; Daniel Preoţiuc-Pietro; David A Asch; H Andrew Schwartz
Journal:  Proc Natl Acad Sci U S A       Date:  2018-10-15       Impact factor: 11.205

9.  Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions.

Authors:  Esaú Villatoro-Tello; Shantipriya Parida; Sajit Kumar; Petr Motlicek
Journal:  Cognit Comput       Date:  2021-07-17       Impact factor: 5.418

10.  Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

Authors:  Bethany Little; Ossama Alshabrawy; Daniel Stow; I Nicol Ferrier; Roisin McNaney; Daniel G Jackson; Karim Ladha; Cassim Ladha; Thomas Ploetz; Jaume Bacardit; Patrick Olivier; Peter Gallagher; John T O'Brien
Journal:  Psychol Med       Date:  2020-01-16       Impact factor: 7.723

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