Artificial Intelligence for Detecting and Predicting Suicide- Related Outcomes on Social Media: A Systematic Review

Artificial intelligence (AI) is increasingly used to detect and predict suicide-related outcomes from social-media data, but the methodological basis and generalizability of this research remain heterogeneous. We conducted a systematic review combining bibliometric analysis with structured document-level synthesis of journal articles indexed in Scopus and Web of Science through 22 May 2026, following PRISMA 2020. A total of 178 studies were included using a 50-field extraction framework. Reddit (51.7%) and Twitter/X (39.3%) were the predominant platforms, while data geography was not reported in 70.8% of studies. Traditional machine learning remained the most frequent model family (69.1%), alongside deep learning (53.9%), Transformer-based models (42.7%), and large language models (9.0%). Suicide-related outcomes and ground-truth sources were heterogeneous. Among studies reporting usable metrics, median F1 was 0.8990 and median AUROC was 0.9480, but external validation was identified in only 26 studies (14.6%). Explainability or interpretability was reported in 58 studies (32.6%); source code was publicly or directly available in 22 (12.4%), and only 14 (7.9%) reported both reusable data and source code. These findings highlight heterogeneous ground-truth practices, limited documented external validation, and uneven reporting of interpretability, ethical procedures, and reusable data and code, constraining assessments of generalizability and reproducibility.