Cognitive networks and text analysis identify anxiety as a key dimension of psychological distress in suicide notes

Abstract

Understanding the mindset of people who end their lives by suicide remains a key research challenge. To this aim, we reconstruct conceptual associations and emotional perceptions of 139 authors of genuine suicide notes, i.e., notes written moments before ending one's own life. Our methods are grounded in cognitive network science, text analysis and psychometrics. We introduce a quantitative framework measuring to what extent authors of suicide notes tend to associate emotional words in ways similar to how 200 individuals with high/low levels of depression, anxiety, and stress (on a DASS-21 scale) recall concepts together. In Study 1, we use text analysis to build one co-occurrence network out of suicide notes' texts and six fluency co-occurrence networks out of fluency data from individuals high/low on anxiety/stress/depression levels. We find that emotional word associations in suicide notes reflect mostly conceptual associations produced by low-anxiety individuals. In Study 2, we perform residual emotional profiling, i.e. measuring the emotional intensity of words remaining after removing from fluency networks those words mentioned and connected in the suicide notes' network. We find that only the high-anxiety fluency network displays negligible emotional residuals, indicating the highest overlap - in terms of emotions - between suicide notes' content and the concepts recalled by individuals with high anxiety levels. We discuss how these findings relate to existing psychological literature, along with their potential implications for understanding and quantifying suicidal behavior.