The Shape of a Final Message: An Emotional Landscape in the Language of Suicide
Pestian, J. P.; Jacobson, D. A.; Pedapati, E. V.; Mendonca, E. A.; McMahon, B. H.; Ive, J.; Glauser, T. A.
Show abstract
The emotional content of suicide notes is typically examined using categorical coding, where each labeled passage is treated in isolation from its surrounding language. In contrast, dimensional models of psychopathology propose that affective content varies along continuous gradients. We evaluated this proposition directly. Excerpts from 884 annotated suicide notes were embedded in a semantic space defined solely by their linguistic properties, and we investigated whether human-assigned emotion labels changed smoothly across this space. They did: affective tone showed clear spatial autocorrelation (Moran's $I = 0.18$, $z = 19.68$, $p < 0.001$), an effect that replicated across three different encoders and remained after removing all within-note dependencies. Emotions occupied recognizable yet overlapping regions rather than forming distinct clusters and varied substantially in how tightly they were concentrated: love and hopelessness appeared with similar frequency, but love was far more localized ($z = 15.7$ versus $10.8$). Among all emotions, hopelessness was the most linguistically diffuse, implying that a single categorical label is capturing multiple, qualitatively different manifestations of suicidal distress.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Solitary Silence and Social Sounds: Music influences mental imagery, inducing thoughts of social interactions 91%
- Improving ascertainment of suicidal ideation and suicide attempt with natural language processing 91%
- Predicting the subjective intensity of imagined sensory experiences from electrophysiological measures of oscillatory brain activity. 90%
Similar papers in this journal
Similar papers in this journal
- Decoding words during sentence production: Syntactic role encoding and structure-dependent dynamics revealed by ECoG 89%
- The Temporal Dynamics of Metacognitive Experiences Track Rational Adaptations in Task Performance 88%
- Asymmetric learning and adaptability to changes in relational structure during transitive inference 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.