Analysis of a Father's Suicide Note: Forensic Stylistics

Penelitian ini menggunakan metode deskriptif kualitatif untuk menganalisis catatan bunuh diri yang ditinggalkan oleh YAP yang mengakhiri hidupnya karena stres akibat hutang judi. Dengan menerapkan teori Prokofyeva tentang ciri-ciri bahasa khas yang terdapat dalam catatan, analisisnya berfokus pada penalaran yang jelas, ekspresi emosional, struktur teks, tata bahasa, dan tanda baca. Catatan bunuh diri ini mengungkapkan rasa putus asa YAP yang mendalam karena ia mengaku merasa tidak mampu membantu keluarganya dan perbuatannya telah menimbulkan banyak masalah. Struktur teks catatan tersebut mengikuti pola yang khas, dengan permintaan maaf awal, penjelasan alasan bunuh diri, ekspresi keprihatinan terhadap anggota keluarga, dan permintaan terakhir. Analisis tata bahasa menyoroti penggunaan waktu untuk menyampaikan tindakan masa lalu dan keadaan saat ini, yang mencerminkan keadaan mental dan niat YAP. Tanda baca dalam catatan tidak secara signifikan mempengaruhi interpretasi pesan YAP tetapi merupakan aspek penting dari analisis linguistik dalam stilistika forensik. Penelitian ini menunjukkan bagaimana stilistika forensik membantu dalam memahami pesan tertulis, terutama dalam kasus catatan bunuh diri berikut. &nbsp

Forensic Stylistic Analysis of UNNES Student’s Suicide Note

Suicide notes are powerful pieces of evidence in suicide cases, left as messages by individuals who commit or intend to commit suicides. The intention of leaving suicide notes is mostly to convey thoughts and feelings that are unknown to people when the victims are alive. This study investigated the linguistic features contained in the suicide note written by a college student of UNNES using descriptive-qualitative as the research method and applying Prokofyeva's theory of suicide notes linguistic features. The suicide note is available and can be accessed online. This study aimed to analyze the forensic stylistic approach towards the suicide note by showing the linguistic features and interpreting the messages delivered by the victim. The researchers classified and described the data in five distinguished characteristics of linguistic features found in the suicide note; clear reasoning, expressing emotions, text structure, grammar, and punctuation. The findings revealed that all the features are present in the suicide note, yet the researchers found that there were different tenses used in the suicide note. The results found only ellipsis in the victim’s suicide note. The data findings in this research may assist future research regarding the study of forensic stylistics, specifically in investigating suicide notes

DASentimental : detecting depression, anxiety, and stress in texts via emotional recall, cognitive networks, and machine learning

Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad−happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts

Detecting an Autobiographical Criminal: Investigating Gender Differences in Staged Suicide Notes

Suicide notes are valuable in assisting equivocal police investigations because they can provide access into the author’s mind. An abundance of research has already linguistically analysed genuine and simulated suicide notes and have identified significant differences between the two. However, this only provides limited assistance in discerning note authenticity. Suicidology research has not yet considered how authors can linguistically construct gender in order to disguise their own, which may underpin some equivocal cases. The present study endeavoured to explore linguistic gender construction in staged suicide notes by cross-referencing suicide, deception and gendered-language corpuses with participants’ self-produced staged suicide notes to determine whether authorship can be detected through language-use and contribute to evaluating suicide note veracity. Participants were student volunteers (n = 4: 2 males, 2 females), recruited from the University of Derby. A qualitative document-interview methodology was used to gain primary data and explore pragmatic meaning by thematically analysing participants’ staged suicide notes and interview transcripts in order to categorise linguistic themes and explore whether pre-existing mental representations can influence language-use. Societal stereotypes regarding suicide and gender were found and linguistic features remained largely consistent with previous research. Findings may improve equivocal suicide investigations

Método semi-supervisado para detectar, clasificar y anotar en un corpus de suicidio textos extraídos de entornos digitales

La presente tesis doctoral, con un enfoque cualicuantitativo (mixto), se enmarca en la línea del análisis de sentimientos en redes sociales, forma parte del proyecto Life, que busca crear una plataforma integral para detectar y brindar apoyo especializado a usuarios de redes sociales que publican textos con contenido suicida. Por ello se desarrolló el Corpus Life para realizar experimentos con algoritmos de aprendizaje automático, mismo que originalmente constaba de 102 mensajes suicidas (71 textos en inglés y 31 textos en español), 70 de estas muestras Sin Riesgo y 32 con Riesgo. Pero debido al escaso número de muestras y al desbalance entre ellas, los resultados generados no eran confiables. Por ello esta investigación tuvo como objetivo general desarrollar un método semi-supervisado para detectar, clasificar y anotar en el Corpus Life, textos extraídos de entornos digitales, con el fin de incrementar su número de anotaciones, mediante un proceso de evaluación automática de su calidad, previo a su inclusión o exclusión. Anotaciones que fueron evaluadas manualmente, utilizando para ello la medida de concordancia Cohen´s Kappa, con la participación de anotadores especializados quienes evaluaron los textos, alcanzando un nivel de acuerdo entre anotadores de 0,86, cercano al 0,78-0,81 de significancia estadística alcanzado automáticamente por medio del índice macro f1, con el método semi-supervisado. Lo que conllevo a alcanzar experimentos de un mayor grado de confiabilidad, por medio de un método estructurado con actividades, roles y procesos bien definidos y enlazados.This doctoral thesis with a qualitative-quantitative (mixed) approach is part of the analysis of feelings in social networks that publish texts with suicidal content. For this reason, Corpus life was developed to carry out experiments with machine learning algorithms, which originally consisted of 102 suicide messages (71 texts in English and 31 texts in Spanish), 70 of these samples without risk and 32 with risk. But due to the small number of samples and the imbalance between them, the generated outcome was not reliable. Therefore, this research had the general objective of developing a semi-supervised method to detect, classify and annotate in the Corpus Life, texts extracted from digital environments, in order to increase their number of annotations, through a process of automatic assessments of their quality, prior to their inclusion or exclusion. Records which were tested manually, using the Cohen's Kappa concordance measure, with the participation of specialized annotators who evaluated the texts, reaching a level of agreement between annotators of 0.86, close to 0.78-0.81 of statistically significant reaching automatically by means of the f1 macro index, with the semi-supervised method. This led to achieving experiments with a higher degree of reliability, through a structured method with well-defined and linked activities, roles and processes

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot