Slang and Self-Harm: A Qualitative Exploration of the Usage of Slang on Reddit Self-Harm Communities

openBackground: Language is an integral part of our daily conversations, shaping thoughts, emotions, and behaviour. Slang forms an integral part of language and is a highly colloquial and informal vocabulary that often has an in-group meaning different from its usual one. This characteristic makes it an integral marker of in-group solidarity for people with shared experiences, such as self-harming. Users in self-harm peer support groups increasingly use slang to communicate their self-harm experience. This study aimed to explore and capture the usage of such slang in self-harm peer support groups to investigate this sharing of experience. Method: Slang terms were analysed from two self-harm peer support groups on Reddit. A list of the most frequently used slang terms was compiled based on two popular posts explaining the slang words. The PRAW Reddit API was used to conduct a preliminary analysis of the most frequently used slang terms from this list and then to source user posts containing the identified slang words. The study excluded posts that did not contain the slang word or used the word in its literal meaning. Conventional content analysis was used to extract codes from the data and identify key concepts and themes. The codes were then grouped into categories and domains for further analysis. Results: 239 and 241 posts were analysed on two Reddit communities. Differences in the words usage between the groups were found, reflecting the groups purposes. Instead, the most common domains ranged from discussing medical care for wounds, seeking support, inquiring about the wound type, and themes of relapse and abstinence. Discussion and Conclusion: This study compiled a list of the most prevalent slang words used within these peer support groups and the key concepts that they portray. The domains analysed provide a foundation for further research on the role of slang in self-harm peer support groups. Clinicians and healthcare professionals can also utilise detailed knowledge about these slang words to penetrate the in-group culture and make meaning of their clients' self-harming experiences. Thus, this study highlights the importance of understanding the role of slang in shaping the perspectives of individuals with shared experiences.Background: Language is an integral part of our daily conversations, shaping thoughts, emotions, and behaviour. Slang forms an integral part of language and is a highly colloquial and informal vocabulary that often has an in-group meaning different from its usual one. This characteristic makes it an integral marker of in-group solidarity for people with shared experiences, such as self-harming. Users in self-harm peer support groups increasingly use slang to communicate their self-harm experience. This study aimed to explore and capture the usage of such slang in self-harm peer support groups to investigate this sharing of experience. Method: Slang terms were analysed from two self-harm peer support groups on Reddit. A list of the most frequently used slang terms was compiled based on two popular posts explaining the slang words. The PRAW Reddit API was used to conduct a preliminary analysis of the most frequently used slang terms from this list and then to source user posts containing the identified slang words. The study excluded posts that did not contain the slang word or used the word in its literal meaning. Conventional content analysis was used to extract codes from the data and identify key concepts and themes. The codes were then grouped into categories and domains for further analysis. Results: 239 and 241 posts were analysed on two Reddit communities. Differences in the words usage between the groups were found, reflecting the groups purposes. Instead, the most common domains ranged from discussing medical care for wounds, seeking support, inquiring about the wound type, and themes of relapse and abstinence. Discussion and Conclusion: This study compiled a list of the most prevalent slang words used within these peer support groups and the key concepts that they portray. The domains analysed provide a foundation for further research on the role of slang in self-harm peer support groups. Clinicians and healthcare professionals can also utilise detailed knowledge about these slang words to penetrate the in-group culture and make meaning of their clients' self-harming experiences. Thus, this study highlights the importance of understanding the role of slang in shaping the perspectives of individuals with shared experiences

The attachment system and physiology in adulthood: normative processes, individual differences, and implications for health.

Attachment theory provides a conceptual framework for understanding intersections between personality and close relationships in adulthood. Moreover, attachment has implications for stress-related physiology and physical health. We review work on normative processes and individual differences in the attachment behavioral system, as well as their associations with biological mechanisms related to health outcomes. We highlight the need for more basic research on normative processes and physiology and discuss our own research on individual differences in attachment and links with physiology. We then describe a novel perspective on attachment and physiology, wherein stress-related physiological changes may also be viewed as supporting the social-cognitive and emotion regulatory functions of the attachment system through providing additional energy to the brain, which has implications for eating behavior and health. We close by discussing our work on individual differences in attachment and restorative processes, including sleep and skin repair, and by stressing the importance of developing biologically plausible models for describing how attachment may impact chronic illness

The Process of Digital Transformation in Education During the COVID-19 Pandemic

Purpose: This document seeks to delve into the digital transformation of education during the COVID-19 pandemic, aiming to provide a comprehensive understanding of this evolving phenomenon's purpose and significance.   Design/Methodology/Approach: The research approach undertaken is characterized by a non-experimental, documentary, exploratory, and descriptive study methodology, which involves an extensive examination of existing literature and data to gain insights into the digital transformation in education during the pandemic.   Findings: The study's key findings revolve around the consensus in existing literature regarding the swift acceleration of the transformation of education from traditional face-to-face classes to virtual learning environments. It also highlights the implications of this transformation, particularly in reshaping teaching models and advocating for a hybrid approach encompassing both face-to-face and virtual learning.   Research, Practical & Social implications: The implications of this research extend to informing educational institutions about the need for digital adaptation, guiding policymakers in supporting adaptable learning models, and empowering educators with a deeper understanding of the changing educational landscape. Moreover, it considers the broader societal impact, including equity and access issues in education.   Originality/Value: This research is unique in its contribution to understanding the profound impact of the COVID-19 pandemic on education. It emphasizes the significance of adaptability and hybrid learning models while providing a foundation for future educational research and policy development

‘Being there’ is what matters:Methodological and ethical challenges when undertaking research on the outdoor environment with older people during and beyond the COVID-19 pandemic

This paper reflects on adapting research methods and processes during the COVID-19 pandemic, drawing on our experiences of conducting research on the outdoor environment with older people (aged 50+) living in Scotland. First, we discuss the challenges to the organisation of research experienced in the context of changing government and university guidelines and managing delays to planned research timelines. The shift toward remote methods stimulated by the pandemic transformed traditional notions of the research field. We consider some of the implications of this for outdoor environment research, grounded as it is on exploring the interaction between people and the places they are embedded within. Further, despite a growth of literature highlighting the benefits of remote research, we found uses for digital and online approaches limited when working with older people. Second, we reflect on whether research with older people in the context of a pandemic can be conducted ethically. Drawing on our research we describe how developing an ‘ethics of care’ included negotiating with formal ethics processes but also the relational, situated ethics of qualitative health research that, because of the pandemic, had begun to shift in new ways. We describe the often intangible impacts of COVID-19 such as social isolation and bereavement that we uncovered as researchers entering into the lives of older people. In closing, we outline some of the key lessons learnt from conducting research on outdoor environments with older people to enable future qualitative health research during and beyond the pandemic

An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors

In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. In this paper we build on our previous work and investigate the use of four concepts: (1

DOTS - detection of covid-19 contagion symptoms and self-diagnosis in social networks

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSocial media present ways for people to share emotions, feelings, ideas, and even symptoms of disease, and is a great source of data for a variety of analyses. At the end of 2019, an alert was raised for a global pandemic of a virus that has a very high contamination rate and can cause respiratory complications in the contaminated people. To help identify those who may have the symptoms of this disease or to control who are already infected, this paper analyzed the performance of KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT algorithms to classify tweets that contained reports of Covid-19 symptoms or selfreports of infection. The dataset was labeled using a set of disease symptom keywords taken from a list provided by the World Health Organization. The tests on these models showed that the Random Forest algorithm performed best when classifying the tweets in a small dataset. This work demonstrated a superior performance of the Random Forest algorithm over other more robust algorithms for this type of classification and dataset.As redes sociais apresentam meios para as pessoas compartilharem emoções, sentimentos, ideias e até sintomas de doenças, e são uma ótima fonte de dados para as mais diversas análises. No final do ano de 2019, um alerta foi levantado para uma pandemia global de um vírus que tem uma taxa de contaminação muito elevada e que pode causar complicações respiratórias nas pessoas contaminadas. Para o auxilio na identificação de pessoas que possam ter os sintomas desssa doença ou o controle das que já estão infectadas, neste trabalho foram analisados os desempenhos dos algoritmos KNN, Naive Bayes, Decision Tree, Random Forest, SVM, Multilayer Perceptron simples, Redes neurais Convolucionais e BERT para classificação de tweets que continham relatos de sintomas do Covid-19 ou auto-declaração de contaminação. O conjunto de dados foi rotulado utilizando um conjunto de palavras chaves dos sintomas da doença retirada de uma lista disponibilizada pela Organização Mundial da Saúde. Os testes nesses modelos mostraram que o algoritmo Random Forest foi o que obteve melhor resultado ao classificar os tweets em uma base de dados pequena. Este trabalho demonstrou o desempenho superior do algoritmo RandomForest sobre outros mais robustos para este tipo de classificação e conjunto de dados