ArticleProceedings of the National Academy of Sciences of the United States of America2024
Language sentiment predicts changes in depressive symptoms.
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 15 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effectiveness of Chatbots in Mental Health Screening and Assessment: Systematic Review.JMIR mental health · 2026Pooled it
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled 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
- Daily Language as an Objective Indicator of Depressive Affective States: A Two-Week Ecological Momentary Assessment Study in Emotional Labor Workers.Psychiatry investigation · 2026Article
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- 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
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- Assessing personality using zero-shot generative AI scoring of brief open-ended text.Nature human behaviour · 2026Article
- Threading the Needle: Practical Considerations for Merging Theory-Driven Computational Psychiatry With Data-Driven Analytics to Enhance Precision Health at Scale.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Language-based assessments can predict psychological and subjective well-being.Communications psychology · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.Npj mental health research · 2025Article
- Artificial intelligence can emulate human normative judgments on emotional visual scenes.Royal Society open science · 2025Article
- Language sentiment predicts changes in depressive symptoms.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The prevalence of depression is a major societal health concern, and there is an ongoing need to develop tools that predict who will become depressed. Past research suggests that depression changes the language we use, but it is unclear whether language is predictive of worsening symptoms. Here, we test whether the sentiment of brief written linguistic responses predicts changes in depression. Across two studies (
Indexed as
Identifiers
What Socratic 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 Socratic graph.