ArticleNature communications2022
Using language in social media posts to study the network dynamics of depression longitudinally.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed, 42 citations in OpenAlex.
- Naturalistic Online Language as a Marker of Depression in Midlife and Older Adults: Computational Text Analysis of Bluesky and Reddit Posts.Journal of medical Internet research · 2026Article
- Towards naturalistic social neuroscience: A multi-level framework integrating real-world phenotyping, neurobiology, and computational mechanisms.Cognitive, affective & behavioral neuroscience · 2026Review
- Smartphone language and resting-state EEG indicators of self-focused attention prospectively predict major depressive disorder risk in adolescents.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- Anticipating depression trajectories by measuring plasticity and change through symptom network dynamics.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Article
- Web-browsing patterns reflect and shape mood and mental health.Nature human behaviour · 2025Article
- AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.Journal of medical Internet research · 2024Review
- 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
- "How" web searches change under stress.Scientific reports · 2024Article
- Article
- Understanding Social Media Information Sharing in Individuals with Depression: Insights from the Elaboration Likelihood Model and Schema Activation Theory.Psychology research and behavior management · 2024Article
- Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure.Frontiers in psychology · 2024Article
- Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia.Frontiers in psychiatry · 2023Article
- Possible Futures for Network Psychometrics.Psychometrika · 2022Article
- Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records.Frontiers in psychiatry · 2022Article
- Using Remotely Supervised At-Home TES for Enhancing Mental Resilience.Frontiers in human neuroscience · 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
2 authors at 1 institution in 3 countries.
Funding
Abstract
Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. We observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.
Indexed as
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.