Search

The increasing usage of social media networks has raised concerns about the growing frequency of cyberbullying incidents. The definition of cyberbullying lacks universal consensus, yet according to several authors, cyberbullying is characterized by aggressive, repetitive, and intentional communication among peers. However, existing cyberbullying detection datasets often focus solely on classifying texts as aggressive or non-aggressive, neglecting the other cyberbullying aspects, thus hindering research progress. This paper proposes a framework for designing a new dataset incorporating all four aspects of cyberbullying to address this gap. The text messages are sourced from a real dataset, while the users' data is generated synthetically. The resulting dataset contains messages exchanged randomly among different pairs of users, thus inculcating repetition. Additionally, the degree of peerness, defined and calculated to measure the likelihood of two users being peers, is used. The intent of harm is quantified as a numeric value using the ratios of aggression and repetition. As a result, the proposed dataset encompasses all four aspects of cyberbullying by providing repeated aggressive messages among users along with quantitative values of the degree of peerness and intent to harm. The proposed dataset is adaptable, with adjustable threshold values for peerness, repetition, and intent to harm, offering flexibility for various applications. The paper concludes by presenting the results of some baseline machine-learning methods on the proposed dataset. • Generation of a semi-synthetic dataset for automatic cyberbullying detection. • The dataset includes all aspects of cyberbullying: aggression, repetition, intent to harm, and occurrence among peers. • Presentation of baseline results on the proposed dataset. [ABSTRACT FROM AUTHOR]

There is some indication that discrimination as well as low levels of life satisfaction render young people at risk of cyberhate victimization. Adopting a socio-ecological perspective, this paper examines whether supportive family, peer and school environments may buffer against the effects of perceived discrimination and low life satisfaction on cyberhate victimization. Data from four countries (N = 3396) of the EU Kids Online IV survey on children aged 11–17 (51% girls) revealed a positive association between perceived discrimination and cyberhate victimization, but this impact was moderated by supportive family and peer environments. A negative association between life satisfaction and cyberhate victimization was mitigated by peer support. However, no associations with the school context were found. The current study provides new insights on how social support on different levels of the social environment may buffer against potential risk factors for cyberhate victimization and can inform decision-makers towards intervention and prevention strategies. [ABSTRACT FROM AUTHOR]

Workplace cyberbullying is a significant problem in higher education institutions. This paper examines faculty self-reports based on victimization, coping, gender, and the Big 5 Personality traits. A cross-sectional survey was distributed via Qualtrics to obtain the perceptions of 179 faculty members. Prevalence results indicated that faculty victims experienced cyberbullying from colleagues/peers, administrators, staff, students, and external members. Results also demonstrate that university faculty are unaware of resources to address cyberbullying. The study found evidence of gender differences in cyberbullying victimization in higher education. Agreeableness and neuroticism were also related to experiencing workplace cyberbullying. The article concludes with a discussion of the study's implications and potential areas of future research. • Most faculty experienced cyberbullying from colleagues/peers, administrators, and students. • Cyberbullied faculty are not likely to report the incident, which make it difficult to be addressed in the future. • Females are more likely to experience cyberbullying across relationship types in comparison to male faculty members. • The personality traits of agreeableness and neuroticism related to workplace cyberbullying. [ABSTRACT FROM AUTHOR]

The past two decades have witnessed an increase in ethnicity-related peer aggression, driven by the intensification of the migration phenomenon and rise of information and communication technologies. The goal of the current study is to extend the scope of previous reviews on peer aggression to examine the emerging evidence on the role of technological tools in the prevention, detection, and handling of ethnicity-related peer aggression among young people. We have identified 14 relevant papers published between 2005 and 2020 that help us answer the following research questions: What forms of aggression among young ethnic minorities do these technological interventions try to address? What types of technological interventions are being used? The results indicate that the technological tools are being used to tackle both intergroup as well as intragroup peer aggression, in which ethnic minorities youth is involved (as a victim, perpetrator or bystander). Most studies have focused on adolescents and young adults in the US or in Europe. The technological tools reported on, include: online games, videos, social media, and chat-based programmes, or machine learning algorithms aimed at tackling online and offline peer aggression. They can be used either as stand - alone tools, or as part of intervention programmes (at the school, family or community level). These findings can be useful to improve and elaborate future digital technologies developed to address ethnicity-related peer aggression. • The review maps the existing literature on the role of technological tools in tackling ethnicity-related peer aggression. • All forms of peer aggression are considered in which ethnic minorities youth was involved. • Three groups of technological interventions are identified and categorized into intra- or intergroup ethnicity-related peer aggression. • The digital tools presented include: online games, video, social media, chat-based programmes, and machine learning algorithms. • Our study reveals some follow-up research questions needed to expand current knowledge on digital technologies. [ABSTRACT FROM AUTHOR]