Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder. Article Swipe
Amanda C Collins
,
Damien Lekkas
,
Matthew D. Nemesure
,
Tess Griffin
,
George D. Price
,
Arvind Pillai
,
Subigya Nepal
,
Michael V. Heinz
,
Andrew T. Campbell
,
Nicholas C. Jacobson
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1037/abn0001003
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1037/abn0001003
Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing techniques have been used to predict depression, with pronoun and emotion usage being identified as important features. However, it is unclear how depressed individuals use positive and negative words when writing about themselves. Individuals with MDD (N = 258) completed ecological momentary assessments three times a day (including the Patient Health Questionnaire-9 [PHQ-9] and a free-text diary entry) and weekly ecological momentary assessments (including a free-text response to a life events prompt) over a 90-day study period. Using natural language processing techniques, we generated 20 model features to detect and predict averages of and changes in weekly depression from diary entries. Four regression models detected and predicted total PHQ-9 and changes in PHQ-9, and two classification models detected and predicted moderate to severe depression. The models classified current (area under the receiver operating curve [AUC] = 0.68) and future depression (AUC = 0.63), and suggest that lower valence increased usage of "I"/"me"/"my," and lower valence of passages with "I"/"me" as the subject, influenced model predictions toward more severe depression, supporting prior research. These findings highlight that depressed individuals use less positive and more negative words when referring to themselves. Treatments targeting positive affect and digital interventions with written components may be beneficial for targeting MDD. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1037/abn0001003
- OA Status
- hybrid
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409332729
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409332729Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1037/abn0001003Digital Object Identifier
- Title
-
Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-10Full publication date if available
- Authors
-
Amanda C Collins, Damien Lekkas, Matthew D. Nemesure, Tess Griffin, George D. Price, Arvind Pillai, Subigya Nepal, Michael V. Heinz, Andrew T. Campbell, Nicholas C. JacobsonList of authors in order
- Landing page
-
https://doi.org/10.1037/abn0001003Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1037/abn0001003Direct OA link when available
- Concepts
-
Depressive symptoms, Sample (material), Psychology, Major depressive disorder, Clinical psychology, Psychiatry, Mood, Cognition, Chromatography, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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