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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2025-06-01
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| author | Massimo Stella Trevor James Swanson Andreia Sofia Teixeira Brianne N. Richson Ying Li Thomas T. Hills Kelsie T. Forbush David Watson |
| author_facet | Massimo Stella Trevor James Swanson Andreia Sofia Teixeira Brianne N. Richson Ying Li Thomas T. Hills Kelsie T. Forbush David Watson |
| author_sort | Massimo Stella |
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| description | 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. |
| format | Article |
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| language | English |
| publishDate | 2025-06-01 |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-2d3ac03a00b6436a878d27221cc1dca62025-07-25T13:14:07ZengMDPI AGBig Data and Cognitive Computing2504-22892025-06-019717110.3390/bdcc9070171Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide NotesMassimo Stella0Trevor James Swanson1Andreia Sofia Teixeira2Brianne N. Richson3Ying Li4Thomas T. Hills5Kelsie T. Forbush6David Watson7CogNosco Lab, Department of Psychology and Cognitive Science, University of Trento, 38121 Trento, ItalyDepartment of Psychology, University of Kansas, Lawrence, KS 66045, USALASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, PortugalDepartment of Psychology, University of Kansas, Lawrence, KS 66045, USAState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, ChinaDepartment of Psychology, University of Warwick, Coventry CV4 7AL, UKDepartment of Psychology, University of Kansas, Lawrence, KS 66045, USADepartment of Psychology, University of Notre Dame, Notre Dame, IN 46556, USAUnderstanding 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.https://www.mdpi.com/2504-2289/9/7/171complex networkstext analysisemotional profilingcognitive network sciencesuicide behaviorpsychological distress |
| spellingShingle | Massimo Stella Trevor James Swanson Andreia Sofia Teixeira Brianne N. Richson Ying Li Thomas T. Hills Kelsie T. Forbush David Watson Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes Big Data and Cognitive Computing complex networks text analysis emotional profiling cognitive network science suicide behavior psychological distress |
| title | Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes |
| title_full | Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes |
| title_fullStr | Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes |
| title_full_unstemmed | Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes |
| title_short | Cognitive Networks and Text Analysis Identify Anxiety as a Key Dimension of Distress in Genuine Suicide Notes |
| title_sort | cognitive networks and text analysis identify anxiety as a key dimension of distress in genuine suicide notes |
| topic | complex networks text analysis emotional profiling cognitive network science suicide behavior psychological distress |
| url | https://www.mdpi.com/2504-2289/9/7/171 |
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