ArticleNPJ digital medicine2022
Machine learning of language use on Twitter reveals weak and non-specific predictions.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Detecting risk for depression and anxiety symptoms among older bereaved women using text-based analysis in the Nurses' Health Study II.American journal of epidemiology · 2026Article
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Active night-time tweeting is associated with meaningfully lower mental wellbeing in a UK birth cohort study.Scientific reports · 2025Article
- Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.Npj mental health research · 2024Article
- A news-based climate policy uncertainty index for China.Scientific data · 2023Article
- Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study.JMIR medical informatics · 2023Article
- Article
- Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling.Biological psychiatry · 2023Review
- Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM.International journal of environmental research and public health · 2022Article
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 at 2 institutions in 2 countries.
Funding
Abstract
Depressed individuals use language differently than healthy controls and it has been proposed that social media posts can be used to identify depression. Much of the evidence behind this claim relies on indirect measures of mental health and few studies have tested if these language features are specific to depression versus other aspects of mental health. We analysed the Tweets of 1006 participants who completed questionnaires assessing symptoms of depression and 8 other mental health conditions. Daily Tweets were subjected to textual analysis and the resulting linguistic features were used to train an Elastic Net model on depression severity, using nested cross-validation. We then tested performance in a held-out test set (30%), comparing predictions of depression versus 8 other aspects of mental health. The depression trained model had modest out-of-sample predictive performance, explaining 2.5% of variance in depression symptoms (R
Identifiers
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.