ArticlePloS one2020
Multimodal mental health analysis in social media.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Article
- Cross-Cultural Differences in Public Discourse on COVID-19 Vaccination in the United States and South Korea: Cross-Sectional Analysis Using Natural Language Processing.JMIR formative research · 2026Article
- Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.PloS one · 2026Article
- Enhancing contact recommendation in social platforms through mental health awareness: Exploring Anorexia Nervosa as a case study.PloS one · 2025Article
- Explainable AI-driven depression detection from social media using natural language processing and black box machine learning models.Frontiers in artificial intelligence · 2025Article
- Multimodal machine learning for language and speech markers identification in mental health.BMC medical informatics and decision making · 2024Article
- Predicting physical and mental health status through interview-based evaluation of work stress: initial attempts to standardize the interviewing method.Industrial health · 2024Article
- The artistic image processing for visual healing in smart city.Scientific reports · 2024Article
- Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.Journal of medical Internet research · 2024Article
- Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities.Journal of healthcare informatics research · 2024Article
- Article
- Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood.PloS one · 2024Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Resilience in Web-Based Mental Health Communities: Building a Resilience Dictionary With Semiautomatic Text Analysis.JMIR formative research · 2022Article
- Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review.Journal of medical Internet research · 2022Article
- Detecting Depression Signs on Social Media: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter.Journal of business research · 2022Article
- Personality of nonprofit organizations' Instagram accounts and its relationship with their photos' characteristics at content and pixel levels.Frontiers in psychology · 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
11 authors.
Funding
Abstract
Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.
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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.