ArticleJournal of medical Internet research2018
Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates.
Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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
35 citing papers in PubMed, 145 citations in OpenAlex.
- Trial
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- Semantic Agency Patterns Signal Depressive Experiences: Evidence From Postpartum Communication on Social Media.Depression and anxiety · 2026Article
- Development and Validation of a Depression Scale for Online Assessment: Cross-Sectional Observational Study.Journal of medical Internet research · 2025Observational
- A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.Npj mental health research · 2025Article
- A Social Media Study of Portrayals of Bipolar Disorders on YouTube: Content and Thematic Analyses.Journal of medical Internet research · 2025Article
- Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.Npj mental health research · 2024Article
- AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.Journal of medical Internet research · 2024Review
- Responsible development of clinical speech AI: Bridging the gap between clinical research and technology.NPJ digital medicine · 2024Review
- The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis.JMIR mental health · 2024Article
- MHA: a multimodal hierarchical attention model for depression detection in social media.Health information science and systems · 2023Article
- Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data.Journal of medical Internet research · 2023Article
- Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models.Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media · 2023Article
- Analyzing User-Generated Web-Based Posts of Adolescents' Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis.Journal of medical Internet research · 2023Article
- Supervised machine learning models for depression sentiment analysis.Frontiers in artificial intelligence · 2023Article
- Predicting Mental Health Status in Remote and Rural Farming Communities: Computational Analysis of Text-Based Counseling.JMIR formative research · 2022Article
- What Are the Characteristics of User Texts and Behaviors in Chinese Depression Posts?International journal of environmental research and public health · 2022Article
- Coming together in a digital age: Community twitter responses in the wake of a campus shooting.PloS one · 2022Article
- Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review.Clinical psychological science : a journal of the Association for Psychological Science · 2022Article
- Analysis and classification of privacy-sensitive content in social media posts.EPJ data science · 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
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFrequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility.
objectiveThe aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates.
methodsStatus updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed.
resultsNegative emotion word instability was a significant predictor of greater depression severity on Facebook (r
conclusionsThe findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform.
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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.