Written Goodbyes: How Genre and Sociolinguistic Factors Influence the Content and Style of Suicide Notes
Authors: Lucia Busso, Claudia Roberta Combei (Aston University, Università di Pavia, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
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.

Introduction
This paper investigates the language of suicide notes, with the goal of uncovering patterns of discourse, topics, and emotional expression across various sociolinguistic factors and relationship dynamics, spanning over 100 years. A suicide note (SN) has been defined in the literature as "any available text by a suicide which was authored shortly before death" ( AUTHOR: 26). \par The importance of a detailed analysis of suicide notes has been acknowledged in the scholarly debate ( AUTHOR). In fact, SNs have been widely studied in linguistics, sociology, and psychology starting with the publication in 1959 of Osgood and Walker's seminal work ( AUTHOR). Since then, the language of SNs has been investigated mainly through Genre Analysis ( AUTHOR), with some scholars working with corpus methods ( AUTHOR). Lately, big corpora of SNs have been collected through the Web and used for computational analyses (inter alia AUTHOR). \par Research on SNs is naturally practical, being focused on suicide prevention ( AUTHOR), identification ( AUTHOR), and authenticity ( AUTHOR). For instance, the study by AUTHOR uses classification algorithms to help mental health professionals distinguish between genuine and elicited suicide notes. This -- the authors claim -- can in turn help developing a prediction strategy of repeated suicide attempts, as suicide notes offer valuable insights into specific personality states and mindsets. Similarly, AUTHOR suggests that analysing SNs may contribute to assessing the risk of repeated suicide attempts. \par Despite the area being well-researched, especially in forensic linguistics, current analyses of SNs present several shortcomings. Given the difficulty of accessing data, scholars have either used dubious source material (such as the letters published on the blog \href{https://theholydark.wordpress.com/2012/08/28/some-painfully-edifying-reading-suicide-notes/}{"The Holy Dark"}), or have reused and reanalysed SNs written by famous people (such as Virginia Woolf and Kurt Cobain, e.g., AUTHOR). Moreover, there is no study to date -- to the best of the authors' knowledge -- that analyses of SNs using text type, which we refer to as genre, or sociolinguistic factors (such as gender, age, addressee, or time period) as covariates. \par In the present paper, we set out to perform corpus and computational analyses on a novel dataset of authentic suicide notes. Specifically, we aim to explore whether and to what extent SNs style and content vary according to genre (letter vs. online post) and sociolinguistic factors (the victim's gender and age, as well as the addressee and time period of the SN). To this end, we employ Structural Topic Modelling ( AUTHOR) and keyword analysis, subjectivity analysis ( AUTHOR), and sentiment and emotion analysis ( AUTHOR).

Conclusion
This mixed-methods study analysed the content and style of 76 SNs written over the course of a century, using genre, several sociolinguistic factors, and relationship dynamics as covariates. First of all, three main topics emerged from our corpus, that we labelled as Explanations, Anguish, and Connectedness. Looking at the differences in topical prevalence between the two text types, we observed that online posts displayed less private feelings (e.g., anguish and pain) and greater polarized emotion words and swearwords. \par Subjectivity analysis revealed that SNs tended to be more subjective than objective, irrespective of the genre. Some differences based on addressees were identified in the corpus; for example, SNs directed toward close relationships (i.e., life-partners and family) showed higher subjectivity scores, suggesting a more profound and personal style, compared to those directed toward the broader (internet) public. \par As far as sentiment analysis is concerned, negative sentiment was dominant in the corpus (i.e. three times more frequent than neutral or positive sentiment), especially in online posts. Then, the analysis of emotions revealed that sadness was the main emotion in the corpus. This evident presence of sadness and negative sentiment reflects the complex emotional challenges and inner struggles that victims experienced at the time they wrote their SNs. Although sadness was the most common emotion in both letters and online posts, it occurred more frequently in the latter text type. Also, letters tended to convey more positive emotions (e.g., joy) more frequently than online posts. Finally, the analysis revealed that sadness was more common in the SNs written by female victims and by teenagers.\par All in all, our results reveal that the content, discourse, and emotional expression in SNs vary as a function of genre, sociolinguistic factors, and relationship dynamics. These differences uncover the need of taking into account specific social, demographic, and cultural variables when designing and implementing suicide prevention and intervention strategies. In this sense, we believe that corpus-based and NLP research on SNs can contribute to the improvement of these personalized strategies. The research presented in this paper was conducted while C. R. Combei benefited from support provided by the project "PON Ricerca e Innovazione 2014–2020 - Linea Innovazione (D.M. 1062/2021)". %%\href{https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/pkfscdkgkhcq}{Overleaf %%template}.