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Network science provides tools for quantifying and reconstructing both semantic frames 18, 19, 20 and emotional associations 12, serving as a framework for the quantitative identification of ways in which people perceive events and happenings 12, 14, 19, 21, 22. In comparison to more opaque machine learning techniques, networks have the advantage of transparently representing a proxy for the associative structure of language in the human mind, within the cognitive system apt at acquiring, storing and producing language, i.e. the mental lexicon 21, 23. Supported by psycholinguistic inquiries into the mental lexicon 14, 23, 24, 25, complex networks built from texts can open a window into people’s mindsets 12. Focus here is given to reconstructing the collective mindset as expressed in the last written words left by people who committed suicide. To this aim, we adopt a corpus of genuine suicide notes gathered in a previous study 9 and including 139 letters from people who committed suicide. The letters come from a collection of suicide notes from sources like newspapers, books and diaries collected by clinical psychologists mostly in the US and in Europe. The notes were written and collected over a time window spanning over 60 years and have been used also for recent machine learning approaches to automatic detection of suicide ideation 9. On average, a letter included 120 ± 12 words and a total of over 2000 different concepts were stated in the whole corpus. Cognitive network approach to suicidal ideation analysis
September 6: Laundrie joins his family on a camping trip about 75 miles from their home in North Port. Furthermore, the neighborhood of “I” is very star-like and its sentiment/valence is significantly more negative (19% of words) than the rest of the network (12.2%), \(p = 0.040\), and less positive (26.6% relative to 36.8%), \(p = 0.020\) ( \(\chi I don’t know what else to say. There are endless things that I could say. But I don’t want to tell you all about the life ahead of you. You need to live your life without hearing it all first. But I hope that this letter may have been a little help to you.
As a clinical psychologist, I probably think about suicide more often and in different ways than most. I’ve read the research. I’ve been trained to ask the hard questions. I am all too familiar with the frustrating gaps in our knowledge base: what causes it, who is at risk, how do we prevent it? I understand the stigma and misconceptions surrounding it, and I know, firsthand, the collateral damage that stems from it. July 2021: Brian Laundrie and his fiancé Gabby Petito leave for a grip across the country. The couple visit numerous national parks across the way.
Her last words on camera: “In keeping with Channel 40’s policy of bringing you the latest in ‘blood and guts’, and in living color, you are going to see another first — attempted suicide.” I urge you to be kind to others. You can not tell by looking at someone if they are struggling with suicidal thoughts or who they’ve lost to suicide. Arm yourself with education and compassion. Suicide is a public health issue and affects us all. Unlike several well-known studies of semantic networks 26, 27 based on semantic associations stored in lexical databases, in our approach networks of associations are extracted directly from raw texts as written down by individual people. As such it can be seen as an extension of map analysis, as used by Carley 28, 29, enriched with: (i) link extraction and analysis based on modern natural language processing and network science metrics, (ii) additional cognitive data about affect patterns used in synergy with network structure, and (iii) linguistic benchmarks relying on recent datasets of conceptual associations (namely, free associations, see next section). These three points represent key ways in which we build upon and extend previous methods. Notice that our ultimate goal is to identify mental and conceptual associations which are on average most typical for suicide notes. Hence, we do not take a sociological perspective focused on evolution of collective narrative strategies in connection with other social processes 30, 31. Instead, we aim at identifying cognitive patterns which are common across different individuals who committed suicide. Using free associations as linguistic benchmarks for text analysis In Fig. 2c,d we present the results. We observe that the overall degree of balance and the frequency of \(\{+,+,+\}\) triads in the CO network and its configuration models are much higher than for the label-shuffled networks (Fig. 2c). In Fig. 2d we further provide evidence that the CO network exhibits a high level of degree of balance through comparison with the FA network. In the free association network there is a more uniform distribution of triad frequencies and the null models follow the decrease of all positive triads in comparison to the co-occurrence network of suicide notes. Emotional balance in suicide notes as a narrative strategy
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