Using Machine Learning to Identify Suicide Risks

In an article published in the journal Nature, researchers explored the detection of suicidal thoughts and behavior (STB) using digital markers derived from an online pro-choice suicide forum.

Study: Using Machine Learning to Identify Suicide Risks. Image Credit: Tero Vesalainen/Shutterstock.com
Study: Using Machine Learning to Identify Suicide Risks. Image Credit: Tero Vesalainen/Shutterstock.com

By analyzing over 3.2 million interactions among 192 individuals, the study developed a machine-learning model to predict high-risk users (HRUs). Key network features like transitivity and density were crucial in identifying HRUs, suggesting that social interaction patterns could indicate heightened suicide risk.

Background

Suicide is a global health crisis, with over 700,000 annual deaths worldwide, and its study is hampered by its sensitive nature. Previous research has utilized online data to analyze STB, but primarily focused on content analysis, leaving social interaction patterns underexplored. Existing studies have shown potential in using social network structures to understand STB, but they are limited by censorship on mainstream platforms.

This paper addressed these gaps by analyzing uncensored social interactions from a pro-suicide online forum, Sanctioned Suicide. The study applied network analysis and machine learning to identify patterns that signal heightened suicide risk, providing novel insights into the dynamics of STB in an environment free from the censorship seen on other platforms.

Network Analysis of High-Risk User Behavior

The researchers focused on analyzing user interactions within the pro-choice suicide forum "Sanctioned Suicide." Data was collected from the "Suicide Discussion" subforum, comprising over 600,000 posts from 11,000 users between March 2018 and February 2021.

A custom Python script was used to gather and anonymize the data. The study aimed to identify the HRUs who were likely to attempt or complete suicide. HRUs were identified based on specific keywords and user activity patterns, leading to the selection of 48 HRUs and 144 control users for comparison.

A network-based approach was used to quantify user interactions within the forum, focusing on thread participation and temporal patterns. Interactions were weighted based on the recency of posts, with the strength of ties decaying over time. This method facilitated the construction of directed weighted graphs for further analysis, helping to identify and understand the behaviors of HRUs within the forum environment.

Egocentric Networks for Risk Prediction

The authors constructed two types of interaction networks using Python's networkX, namely, thread-specific and thread-agnostic. Thread-specific networks focused on interactions within individual threads, while the thread-agnostic network aggregated interactions across all threads.

Egocentric networks, centered around individual users (egos) and their immediate connections (alters), were then extracted. These networks considered both inward and outward-directed edges, with edge weights indicating the strength of connections. Structural features of these egocentric networks, such as centrality measures, were calculated to characterize user engagement.

These 17 features were utilized in a machine learning model developed in R using the caret package. The model aimed to classify HRUs in the online suicide forum by analyzing interaction patterns. To address the class imbalance, the synthetic minority oversampling technique (SMOTE) was employed.

The model's performance was validated using cross-validation and evaluated on a held-out test set, with metrics including sensitivity and area under the curve (AUC). Shapley additive explanations (SHAP) were used to interpret the model's predictions, highlighting key features influencing outcomes. The study's code and data were made available for transparency and reproducibility.

Key Findings and Implications

The researchers assessed the predictive performance of a machine learning model using network-based features to identify HRUs for suicide on the "Sanctioned Suicide" forum. The model achieved an AUC of 0.73 in both cross-validation and test sets, with respective sensitivities and specificities of around 0.70.

The analysis revealed that egocentric network features, such as lower density, higher transitivity, and lower in-degree centrality, were significant predictors of HRU status. The authors highlighted that users with sparse, triadic networks and low centrality in social interactions were more likely to be at risk. Conversely, those more integrated within the community were less likely to be HRUs.

The SHAP analysis provided insight into the relative importance of these features in predicting suicidal behavior. The findings suggested that social network patterns could serve as critical indicators of suicide risk, though further research is needed to generalize these results across different platforms and consider the context of interactions. The study underscored the potential of incorporating network-based features into suicide prevention efforts on social media.

Conclusion

In conclusion, the researchers demonstrated the potential of using network-based features to predict HRUs for suicide on an online pro-choice suicide forum. By analyzing over 3.2 million interactions, researchers developed a machine-learning model that achieved an AUC of 0.73.

Key findings indicated that HRUs tend to have sparse networks with higher transitivity and lower centrality, suggesting that social interaction patterns could signal heightened suicide risk. These results highlighted the value of incorporating social network analysis into suicide prevention strategies, though further research is necessary to validate these findings across different platforms.

Journal reference:
  • Lekkas, D., & Jacobson, N. C. (2024). Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning. Scientific Reports14(1). DOI: 10.1038/s41598-024-70282-0, ‌https://www.nature.com/articles/s41598-024-70282-0
Soham Nandi

Written by

Soham Nandi

Soham Nandi is a technical writer based in Memari, India. His academic background is in Computer Science Engineering, specializing in Artificial Intelligence and Machine learning. He has extensive experience in Data Analytics, Machine Learning, and Python. He has worked on group projects that required the implementation of Computer Vision, Image Classification, and App Development.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Nandi, Soham. (2024, September 02). Using Machine Learning to Identify Suicide Risks. AZoAi. Retrieved on December 10, 2024 from https://www.azoai.com/news/20240902/Using-Machine-Learning-to-Identify-Suicide-Risks.aspx.

  • MLA

    Nandi, Soham. "Using Machine Learning to Identify Suicide Risks". AZoAi. 10 December 2024. <https://www.azoai.com/news/20240902/Using-Machine-Learning-to-Identify-Suicide-Risks.aspx>.

  • Chicago

    Nandi, Soham. "Using Machine Learning to Identify Suicide Risks". AZoAi. https://www.azoai.com/news/20240902/Using-Machine-Learning-to-Identify-Suicide-Risks.aspx. (accessed December 10, 2024).

  • Harvard

    Nandi, Soham. 2024. Using Machine Learning to Identify Suicide Risks. AZoAi, viewed 10 December 2024, https://www.azoai.com/news/20240902/Using-Machine-Learning-to-Identify-Suicide-Risks.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoAi.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Boost Machine Learning Trust With HEX's Human-in-the-Loop Explainability