TY - GEN
T1 - Written Goodbyes: How Genre and Sociolinguistic Factors Influence the Content and Style of Suicide Notes
AU - Busso, Lucia
AU - Combei, Claudia Roberta
N1 - Copyright © 2024 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/
PY - 2024/12/4
Y1 - 2024/12/4
N2 - The study analyses a novel corpus of 76 freely available English authentic suicide notes (SNs) (letters and social media posts), spanning from 1902 to 2023. By using NLP and corpus linguistics tool, this research aims at decoding patterns of content and style in SNs. In particular, we explore variation in linguistic features in SNs across sociolinguistic factors (age, gender, addressee, time period) and between text type - referred to as genre - (letters vs. online posts). To this end, we use topic models, subjectivity analysis, and sentiment and emotion analysis. Results highlight how both discourse and emotion expression, show differences depending on genre, gender, age group and time period. We suggest a more nuanced approach to personalized prevention and intervention strategies based on insights from computer-assisted linguistic analysis.
AB - The study analyses a novel corpus of 76 freely available English authentic suicide notes (SNs) (letters and social media posts), spanning from 1902 to 2023. By using NLP and corpus linguistics tool, this research aims at decoding patterns of content and style in SNs. In particular, we explore variation in linguistic features in SNs across sociolinguistic factors (age, gender, addressee, time period) and between text type - referred to as genre - (letters vs. online posts). To this end, we use topic models, subjectivity analysis, and sentiment and emotion analysis. Results highlight how both discourse and emotion expression, show differences depending on genre, gender, age group and time period. We suggest a more nuanced approach to personalized prevention and intervention strategies based on insights from computer-assisted linguistic analysis.
KW - sentiment and emotion analysis
KW - subjectivity analysis
KW - suicide notes
KW - topic modelling
UR - http://www.scopus.com/inward/record.url?scp=85214357135&partnerID=8YFLogxK
UR - https://ceur-ws.org/Vol-3878/
M3 - Conference publication
AN - SCOPUS:85214357135
VL - 3878
T3 - CEUR Workshop Proceedings
BT - Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024): Pisa, Italy, December 4-6, 2024.
A2 - Dell'Orletta, Felice
A2 - Lenci, Alessandro
A2 - Montemagni, Simonetta
A2 - Sprugnoli, Rachele
PB - CEUR-WS.org
T2 - 10th Italian Conference on Computational Linguistics, CLiC-it 2024
Y2 - 4 December 2024 through 6 December 2024
ER -