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• W3202563048 abstract "Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is love, which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of love combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention." @default.
• W3202563048 created "2021-10-11" @default.
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• W3202563048 date "2021-09-30" @default.
• W3202563048 modified "2023-09-23" @default.
• W3202563048 title "Revealing semantic and emotional structure of suicide notes with cognitive network science" @default.
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• W3202563048 doi "https://doi.org/10.1038/s41598-021-98147-w" @default.
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