Interpretable machine learning-based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data.

Abstract

We aimed to identify and understand risk and protective factors for suicide among South Korean females by linking survey and social media data and using interpretable machine learning approaches. We collected a wide range of potential factors including the material, psychosocial, and behavioral data from a detailed survey, which we then linked to data from social media. In addition, we adopted interpretable machine learning approaches to (1) predict the suicide risk, (2) explain the relative importance of factors and their interactions regarding suicide, and (3) understand individual differences affecting suicide risk. The best-performing machine learning model achieved an AUC of 0.737. Adverse childhood experiences, social connectedness, and mean positive sentiment score of social media posts were the three risk factors that had a monotonic or unimodal relationship with suicide, and satisfaction with life, narcissistic self-presentation, and number of close friends on social media were the three protective factors that had a monotonic or unimodal relationship with suicide. We also found several meaningful interactions between specific psychiatric symptoms and narcissistic self-presentation. Our findings can help governmental organizations to better assess female suicide risk in South Korea and develop more informed and customized suicide prevention strategies.