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.
Highlights
Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge
These studies have focused on answering the question: what is in a suicide note? That is, what are the contents that we most consistently observe when comparing notes from people that committed suicide? For example, Al-Mosaiwi and Johnstone[7] recently found that the vocabulary used by individuals at risk of suicide was different from those who suffered from other mental disorders related
To study the emotional balance structure of mental states in suicide notes we start by building a signed network from the CO network; we evaluate its triad frequency and degree of balance—i.e., fraction of balanced triads; we present their statistical significance by comparing observed data against two different null models, as well as against the free associations (FA) network, proceeding as with the CO network
Summary
Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. Sentiment analysis has been further applied to the goal of comparing how the emotional contents of suicide notes are categorized by learning algorithms versus trained clinicians[8,9], as well as whether or not such algorithms can reliably distinguish between genuine and simulated suicide notes[10]. These automated text-analysis techniques offer some powerful advantages over the standard, qualitative approaches that have commonly been applied to the study of suicide notes by clinical psychologists. Complex statistical/machine learning models often produce results that are difficult to understand and may not be very helpful for tasks different than prediction such as e xplanation[13]
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