Analyzing Language in Suicide Notes and Legacy Tokens

Egnoto, Michael J.; Griffin, Darrin J.

doi:10.1027/0227-5910/a000363

Cited by 14 publications

(5 citation statements)

References 32 publications

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“…Using measures derived from psychological linguistic theories, automated linguistic analysis of suicide-related posts may help to better differentiate the users who require support from those who do not (Luxton, June, & Kinn, 2011). Such analyses are not new (Ogilvie, Stone, & Shneidman, 1966) and have been shown to provide a valid measure of suicidality (Egnoto & Griffin, 2016;Pestian, Nasrallah, Matykiewicz, Bennett, & Leenaars, 2010), confirming that language is a reliable way to measure people's internal thoughts. In previous analyses of text written by those who have died by suicide, linguistic styles and patterns are inconsistent.…”

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confidence: 99%

A Linguistic Analysis of Suicide-Related Twitter Posts

O’Dea

Larsen

Batterham

et al. 2017

Crisis

111

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confidence: 99%

A Linguistic Analysis of Suicide-Related Twitter Posts

O’Dea

Larsen

Batterham

et al. 2017

Crisis

111

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show abstract

“…Closely paralleling our findings, an idiographic study of the diary of Italian writer Cesare Pavese, for instance, found a gradual increase of FPSP in the year that preceded his suicide [ 14 ]. Furthermore, suicide notes contained more personal pronouns than legacy tokens of spree killers or student writings [ 15 ]. Furthermore, reductions of depressive symptoms were associated with reductions of the use of “I” over time in an expressive writing treatment for female survivors of childhood sexual abuse [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

Paint it Black: Using Change-Point Analysis to Investigate Increasing Vulnerability to Depression towards the End of Vincent van Gogh’s Life

Emmerik

Hamaker

2017

Healthcare

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This study investigated whether Vincent van Gogh became increasingly self-focused—and thus vulnerable to depression—towards the end of his life, through a quantitative analysis of his written pronoun use over time. A change-point analysis was conducted on the time series formed by the pronoun use in Van Gogh’s letters. We used time as a predictor to see whether there was evidence for increased self-focus towards the end of Van Gogh’s life, and we compared this to the pattern in the letters written before his move to Arles. Specifically, we examined Van Gogh’s use of first person singular pronouns (FPSP) and first person plural pronouns (FPPP) in the 415 letters he wrote while working as an artist before his move to Arles, and in the next 248 letters he wrote after his move to Arles until his death in Auvers-sur-Oise. During the latter period, Van Gogh’s use of FPSP showed an annual increase of 0.68% (SE = 0.15, p < 0.001) and his use of FPPP showed an annual decrease of 0.23% (SE = 0.04, p < 0.001), indicating increasing self-focus and vulnerability to depression. This trend differed from Van Gogh’s pronoun use in the former period (which showed no significant trend in FPSP, and an annual increase of FPPP of 0.03%, SE = 0.02, p = 0.04). This study suggests that Van Gogh’s death was preceded by a gradually increasing self-focus and vulnerability to depression. It also illustrates how existing methods (i.e., quantitative linguistic analysis and change-point analysis) can be combined to study specific research questions in innovative ways.

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“…We used an existing anonymised dataset (Kernot, Bossomaier, & Bradbury, 2017b) that comprises 35 normal bloggers and 25 suicide notes and final manifestos from attacks in the USA, Germany, Canada, Brazil, and Finland. Data were processed with the Linguistic Inquiry and Word Count (LIWC) tool (Pennebaker et al, 2015) and we use the negative emotion and anger sentiment tags that are known to differentiate the writing of normal students from suicide attackers (Egnoto & Griffin, 2016). It was also processed for human senses using RPAS, a technique that identifies self from neurolinguistics indicators (Kernot, Bossamier, & Bradbury, 2018).…”

Section: Methodsmentioning

confidence: 99%

An embodied cognition classifier of human emotion and senses

2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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Drawing on the idea of embodied cognition, where the human body and its environment influence the way a person thinks and feels, we test if it is possible to classify suicide attackers from their writing by using words affected by the emotional and modality-specific systems in the brain. We use their final notes and manifestos compared with normal bloggers' posts to train a machine learning classifier. We compare two support vector machine classifier models using linear and radial base function kernels. In this exploratory study, receiver operating characteristic curves show encouraging separation accuracies. These models offer an 11-13% improvement over methods using only emotion or sense categories. This study supports the idea that an embodied cognition classifier better discriminates the way a person thinks and feels rather than treating the body and mind as separate entities and may help in reflecting behavior and applying influence in online social systems.

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Analyzing Language in Suicide Notes and Legacy Tokens

Cited by 14 publications

References 32 publications

A Linguistic Analysis of Suicide-Related Twitter Posts

A Linguistic Analysis of Suicide-Related Twitter Posts

Paint it Black: Using Change-Point Analysis to Investigate Increasing Vulnerability to Depression towards the End of Vincent van Gogh’s Life

An embodied cognition classifier of human emotion and senses

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