Beyond text: Using network centralities and AI models to detect suicide risk on Reddit

Area of Science:

• Social Network Analysis
• Computational Psychiatry
• Digital Mental Health

Background:

• Suicide prevention research increasingly uses social media data.
• Most studies focus on textual analysis, neglecting network structures.
• Online social network characteristics offer underexplored suicide risk signals.

Purpose of the Study:

• To investigate if network centrality metrics from Reddit interaction graphs can detect suicide risk.
• To assess if graph-based features offer complementary signals to text-centric approaches.
• To explore the interplay between network structure and textual content in suicide risk detection.

Main Methods:

• Constructed directed user interaction graphs from Reddit data.
• Extracted 14 network centrality metrics (e.g., degree, betweenness, eigenvector).
• Employed machine learning classifiers and a Graph Neural Network (GNN) framework integrating Sentence-BERT (SBERT) embeddings.

Main Results:

• Network centrality metrics alone provided meaningful signals for suicide risk detection.
• The best-performing GNN with SBERT achieved 67% accuracy and 67% F1-score.
• Graph-based features revealed patterns of isolation, influence, and social connectedness in vulnerable users.

Conclusions:

• Structural network analysis offers complementary value to text-based methods in suicide risk detection.
• Network centrality metrics can identify social patterns indicative of user vulnerability.
• Findings are preliminary due to small dataset size and keyword-based sampling; further research is warranted.