ArticleProceedings of the National Academy of Sciences of the United States of America2024
Key language markers of depression on social media depend on race.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- The Role of Artificial Intelligence in Clinical Psychology: How AI and NLP Systems Are Reshaping Psychological Interventions. A Systematic Review.Clinical psychology & psychotherapyPooled it
- Assessing the Need for Mental Health Support From Free-Text Responses: Development and Validation of Language-Based Assessments in Adults With Internalizing Symptoms.JMIR mental health · 2026Article
- Depressive Symptoms Are Associated with Linguistic Features of Negative but Not Positive Autobiographical Narratives.Affective science · 2026Article
- Interpretable Topic Modeling of Spontaneous Speech in Depression Using Large Language Models: Multilingual Four-Cohort Study.JMIR mental health · 2026Article
- Speech markers of psychological change following a psychedelic 5-MeO-DMT retreat.Journal of psychopharmacology (Oxford, England) · 2026Article
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Early detection of mental health on social media using a hybrid Bi-LSTM-XGBoost model: a comparative study.Scientific reports · 2026Article
- PsychiatryBench: a multi-task benchmark for LLMs in psychiatry.NPJ digital medicine · 2026Article
- Language-based assessments can predict psychological and subjective well-being.Communications psychology · 2026Article
- Digital Mental Health Through an Intersectional Lens: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Putting equity first: reorienting artificial intelligence for population-level postpartum depression prevention.Frontiers in public health · 2026Article
- Multimodal machine learning for video based single question mental health assessment.NPJ digital medicine · 2025Article
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- Artificial intelligence in mental health: integrating opportunities and challenges of multimodal deep learning for mental disorder prevention and treatment.Annals of medicine and surgery (2012) · 2025Review
- The construction of emotional meaning in language.Communications psychology · 2025Review
- Analysis of social media language reveals the psychological interaction of three successive upheavals.Scientific reports · 2025Article
- Improving topic modeling performance on social media through semantic relationships within biomedical terminology.PloS one · 2025Article
- From theDepression and anxiety · 2025Article
- "I Been Taking Adderall Mixing it With Lean, Hope I Don't Wake Up Out My Sleep": Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.