Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis.

ABSTRACT:

Background

Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research.

Objective

This review aims to assess the effectiveness of user-generated social media texts in predicting depression and evaluate the influence of demographic, language, social media activity, and temporal features on predicting depression on social media texts through machine learning.

Methods

We searched studies from 11 databases (CINHAL [through EBSCOhost], PubMed, Scopus, Ovid MEDLINE, Embase, PubPsych, Cochrane Library, Web of Science,

SUBMITTER: Phiri D 

PROVIDER: S-EPMC12032503 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature