Researchdeep learningembeddingsdepression detection
Text-Based Depression Models Demonstrate Strong Predictive Performance
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8.2
Authors systematically reviewed text-based depression estimation studies (searching 2014–2025) and meta-analyzed 15 models from 11 studies, reporting a pooled correlation r=0.605. Embedding-based features and deep-learning architectures (r≈0.74 and r≈0.73) outperformed traditional features and shallow models, and clinician-diagnosed labels yielded higher performance than self-reports; reporting quality was positively associated with performance.
Scoring Rationale
Systematic meta-analysis with peer-reviewed evidence and practical guidance; limited novelty beyond consolidating existing methods and benchmarks.
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