ArticleThe Journal of supercomputing2022
Automatic detection of depression symptoms in twitter using multimodal analysis.
Article in The Journal of supercomputing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 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
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- DynMultiDep: A Dynamic Multimodal Fusion and Multi-Scale Time Series Modeling Approach for Depression Detection.Journal of imaging · 2026Article
- Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models.Frontiers in digital health · 2026Article
- A novel and efficient personalized stress detection technique using a deep learning model.Scientific reports · 2025Article
- Understanding social media discourse on antidepressants: unsupervised and sentiment analysis using X.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Feature-based enhanced boosting algorithm for depression detection.PeerJ. Computer science · 2025Article
- Using YouTube Comments Data to Explore Postpartum Depression in Social Media: An Infodemiology Study.International journal of environmental research and public health · 2024Article
- Postgraduate psychological stress detection from social media using BERT-Fused model.PloS one · 2024Article
- Mental illness detection through harvesting social media: a comprehensive literature review.PeerJ. Computer science · 2024Article
- Automatic diagnosis of depression based on attention mechanism and feature pyramid model.PloS one · 2024Article
- MHA: a multimodal hierarchical attention model for depression detection in social media.Health information science and systems · 2023Article
- Textual Analysis of Tweets Associated with Domestic Violence.Iranian journal of public health · 2023Article
- Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.Cognitive neurodynamics · 2022Review
- Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication.Cognitive computation · 2022Article
- Detecting Depression Signs on Social Media: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- An optimized deep learning approach for suicide detection through Arabic tweets.PeerJ. Computer science · 2022Article
- Advancing translational research in neuroscience through multi-task learning.Frontiers in psychiatry · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user's psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information.
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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.