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Social Media-Based Depression Prediction and Assessment: Methods, Challenges, and Future Directions
CHEN Meifen
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DOI:10.17265/2161-623X/2026.05.012
Shenzhen Polytechnic University, Shenzhen, China
Depression is a common mental disorder and a major global public health concern. The widespread use of social media has created new opportunities for the early recognition and supplementary assessment of depression-related risk signals, because users frequently disclose their emotions, behaviors, and interpersonal experiences online. This review synthesizes the literature on social-media-based depression detection, with an emphasis on the major categories of features used in this field, including textual, behavioural, temporal, emotional, visual, and multimodal signals. It further summarizes the principal modeling strategies, ranging from shallow machine-learning classifiers to deep-learning and attention-based architectures. Recent bibliometric, systematic review, and meta-analytic evidence indicates that this field continues to grow and that prediction performance is often promising, although substantial heterogeneity remains across platforms, labeling strategies, feature sets, task definitions, and evaluation metrics. Overall, social-media-based depression assessment has considerable value as a supplementary screening approach, particularly for early risk identification and longitudinal monitoring. However, it should not be regarded as a substitute for clinical diagnosis. Future research should place greater emphasis on label validity, feature validity, model interpretability, cross-platform generalizability, ethical governance, and clinical translation.
social network, depression, prediction, assessment method
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