Revealing semantic and emotional structure of suicide notes with cognitive network science
Not on the map. Why?
Abstract (arXiv)
Understanding the cognitive and emotional perceptions of people who commit suicide is one of the most sensitive scientific challenges. There are circumstances where people feel the need to leave something written, an artifact where they express themselves, registering their last words and feelings. These suicide notes are of utmost importance for better understanding the psychology of suicidal ideation. This work gives structure to the linguistic content of suicide notes, revealing interconnections between cognitive and emotional states of people who committed suicide. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of the mindset expressed in suicide notes. Our cognitive network representation enables the quantitative analysis of the language in suicide notes through structural balance theory, semantic prominence and emotional profiling. Our results indicate that the emotional syntax connecting positively- and negatively-valenced terms gives rise to a degree of structural balance that is significantly higher than null models where the affective structure was randomized. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. A key positive concept is "love", which integrates information relating the self to others in ways that are semantically prominent across suicide notes. The emotions populating the semantic frame of "love" combine joy and trust with anticipation and sadness, which connects with psychological theories about meaning-making and narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes informing future research for suicide prevention.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
6 papers.
- 2025Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluationcited by 13
- 2021DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learningcited by 6
- 2021Cognitive network science for understanding online social cognitions: A brief reviewcited by 5
- 2022Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaignscited by 3
- 2022Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notescited by 3
- 2021Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communitiescited by 2
- 2025Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation25 Feb 2025
- 2022Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes20 May 2022
- 2022Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns19 Jan 2022
- 2021Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities28 Oct 2021
- 2021DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning26 Oct 2021
- 2021Cognitive network science for understanding online social cognitions: A brief review25 Feb 2021