Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes

Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes

Understanding the mindset of people who die by suicide remains a key research challenge. We map conceptual and emotional word–word co-occurrences in 139 genuine suicide notes and in reference word lists, an Emotional Recall Task, from 200 individuals grouped by high/low depression, anxiety, and stre...

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Bibliographic Details
Main Authors: Massimo Stella, Trevor James Swanson, Andreia Sofia Teixeira, Brianne N. Richson, Ying Li, Thomas T. Hills, Kelsie T. Forbush, David Watson
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Big Data and Cognitive Computing
Subjects:
complex networks
text analysis
emotional profiling
cognitive network science
suicide behavior
psychological distress
Online Access:https://www.mdpi.com/2504-2289/9/7/171
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Summary:Understanding the mindset of people who die by suicide remains a key research challenge. We map conceptual and emotional word–word co-occurrences in 139 genuine suicide notes and in reference word lists, an Emotional Recall Task, from 200 individuals grouped by high/low depression, anxiety, and stress levels on DASS-21. Positive words cover most of the suicide notes’ vocabulary; however, co-occurrences in suicide notes overlap mostly with those produced by individuals with low anxiety (Jaccard index of 0.42 for valence and 0.38 for arousal). We introduce a “words not said” method: It removes every word that corpus A shares with a comparison corpus B and then checks the emotions of “residual” words in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mo>−</mo><mi>B</mi></mrow></semantics></math></inline-formula>. With no leftover emotions, A and B are similar in expressing the same emotions. Simulations indicate this method can classify high/low levels of depression, anxiety and stress with 80% accuracy in a balanced task. After subtracting suicide note words, only the high-anxiety corpus displays no significant residual emotions. Our findings thus pin anxiety as a key latent feature of suicidal psychology and offer an interpretable language-based marker for suicide risk detection.
ISSN:2504-2289

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