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Updated: Jul 3, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

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

Golnaz Nikmehr¹, Aritz Bilbao-Jayo¹, Aitor Almeida¹

¹Deustotech, University of Deusto, Bilbao, Spain.

International Journal of Clinical and Health Psychology : IJCHP

|July 2, 2026

Summary

This summary is machine-generated.

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Network structure analysis on Reddit can help detect suicide risk. Graph centrality metrics reveal social patterns in vulnerable users, complementing text-based methods for improved detection.

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).

Keywords:

Centrality Deep learning Graph neural networks Machine learning Social media Social networks Suicidal ideation detection

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Last Updated: Jul 3, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

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.