ArticleHumanities & social sciences communications2022
A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community.
Article in Humanities & social sciences communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Ensemble of transformers for depression emotion classification.Cognitive neurodynamics · 2026Article
- Cyberbullying victimization identification and large language model-assisted assessment: a study of cyberbullying victimization lexicon construction and validation.Frontiers in psychiatry · 2026Article
- Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.PloS one · 2026Article
- Enhancing TextGCN for depression detection on social media with emotion representation.Frontiers in psychology · 2025Article
- MOODMIND: A Pilot Feasibility Study of Artificial Intelligence for Major Depressive Disorder Screening in Tuberculosis Patients.F1000Research · 2025Article
- Article
- Depression detection with machine learning of structural and non-structural dual languages.Healthcare technology letters · 2024Article
- Federated learning for privacy-preserving depression detection with multilingual language models in social media posts.Patterns (New York, N.Y.) · 2024Article
- Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.JMIR cancer · 2024Article
- Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.Journal of medical Internet research · 2023Article
- Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach.JMIR AI · 2023Article
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
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3 authors.
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Abstract
Globally, the number of people who suffer from depression is consistently increasing. Because both detecting and addressing the early stage of depression is one of the strongest factors for effective treatment, a number of scholars have attempted to examine how to detect and address early-stage depression. Recent studies have been focusing on the use of social media for depression detection where users express their thoughts and emotions freely. With this trend, we examine two-step approaches for early-stage depression detection. First, we propose a depression post-classification model using multiple languages
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