Observational studyJournal of affective disorders2022
The relationship between text message sentiment and self-reported depression.
Observational study in Journal of affective disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Incorporating Data From Text Messages and Email Into Psychotherapy: Randomized Controlled Trial.JMIR mental health · 2026Trial
- Fine-tuning large language models for detecting negative self-referential language in smartphone text among youth with depression.Journal of psychopathology and clinical science · 2026Article
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- Comparing training window selection methods for prediction in non-stationary time series.The British journal of mathematical and statistical psychology · 2026Article
- In their own words: case studies of adolescent smartphone language preceding suicide-related hospitalizations.NPP - digital psychiatry and neuroscience · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Language-Based Detection of Depression with Machine Learning: Systematic Review and Meta- Analysis.Research square · 2025Article
- Testing the feasibility of passive sensing among adolescents: Implications for mental health.Journal of psychopathology and clinical science · 2025Article
- Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.Journal of psychopathology and clinical science · 2025Article
- Comparative Efficacy of MultiModal AI Methods in Screening for Major Depressive Disorder: Machine Learning Model Development Predictive Pilot Study.JMIR formative research · 2025Article
- Mental Health and Mobile Communication Profiles of Crowdsourced Participants.IEEE journal of biomedical and health informatics · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Digital phenotyping of depression during pregnancy using self-report data.Journal of affective disorders · 2024Observational
- Language sentiment predicts changes in depressive symptoms.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Detecting adolescent depression through passive monitoring of linguistic markers in smartphone communication.Journal of child psychology and psychiatry, and allied disciplines · 2024Article
- Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.Npj mental health research · 2024Article
- Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.Npj mental health research · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundPersonal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers.
methodsWe correlated privacy-preserving sentiment analysis of text messages with self-reported depression symptom severity. We enrolled 219 U.S. adults in a 16 week longitudinal observational study. Participants installed a personal sensing app on their phones, which administered self-report PHQ-8 assessments of their depression severity, collected phone sensor data, and computed anonymized language sentiment scores from their text messages. We also trained machine learning models for predicting end-of-study self-reported depression status using on blocks of phone sensor and text features.
resultsIn correlation analyses, we find that degrees of depression, emotional, and personal pronoun language categories correlate most strongly with self-reported depression, validating prior literature. Our classification models which predict binary depression status achieve a leave-one-out AUC of 0.72 when only considering text features and 0.76 when combining text with other networked smartphone sensors. LIMITATIONS: Participants were recruited from a panel that over-represented women, caucasians, and individuals with self-reported depression at baseline. As language use differs across demographic factors, generalizability beyond this population may be limited. The study period also coincided with the initial COVID-19 outbreak in the United States, which may have affected smartphone sensor data quality.
conclusionsEffective depression prediction through text message sentiment, especially when combined with other personal sensors, could enable comprehensive mental health monitoring and intervention.
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