Developing a cyberbullying conceptual framework for educators

Redmond, Petrea; Lock, Jennifer; Smart, Victoria

doi:10.1016/j.techsoc.2019.101223

Cited by 31 publications

(28 citation statements)

References 29 publications

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“… (Independent Education Union 2011 ; Redmond et al 2020 ) 2 Includes Cyberbullying definition The definition includes the repetition of an action and the intention to cause harm. (Chalmers et al 2016 ; Independent Education Union 2011 ; Redmond et al 2020 ; Smith et al 2012 ) 3 Includes a list of cyber-risks forms Includes, but is not limited to, phishing, data and identity theft, infection of systems by malware, cyber sexual predation, exposure to inappropriate content, and cyberbullying. (Independent Education Union 2011 ; Redmond et al 2020 ; Smith et al 2012 ) 4 Includes a list of cyberbullying forms Includes, but is not limited to, exclusion or peer rejection, harassment, or ‘cyberstalking’, outing, impersonation and fake profiles.…”

Section: Methodsmentioning

confidence: 99%

“…The authors also emphasized on the need to critically evaluate the effectiveness of policies to determine if they are succeeding in reducing cyber-crimes and cyberbullying incidents. More recently, Redmond et al ( 2020 ) developed a cyberbullying conceptual framework for educators. In their work, the authors reviewed the literature on cyberbullying in schools to identify common terms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to many studies, the risks of being exposed to cybercrimes have increased due to the Covid-19 pandemic (Jain et al 2020 ; Medina et al 2020 ). Therefore, the urgency of having well defined policies to protect the children has increased as well (Kozma and Isaacs 2011 ; Redmond et al 2020 ). Consequently, educational authorities and institutions had to re-think their e-safety guidelines and re-study their current national curricula and policies.…”

Section: Introductionmentioning

confidence: 99%

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Cyber-Safety Policy Elements in the Era of Online Learning: A Content Analysis of Policies in the UAE

Siyam

Hussain

2021

TechTrends

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With the increased level of technology usage in schools and the move to online learning, many schools had to re-evaluate the content of their cyber-safety policy and review it to ensure it works within and beyond the schools’ premises. This study aimed to analyse the cyber-safety policies of twenty private schools in Dubai, an emirate in the United Arab Emirates (UAE). Five main categories were considered for the content analysis of the policy documents including definitions, preventive measures, reporting and responding to incidents, connection to other policies and mention of existing legislation. Upon the analysis of the policy documents, it was found that while some addressed cyber-safety issues, the focus remained more on cyberbullying incidents. Besides, the development of the cyber-safety policies is lacking the input from the concerned authorities whose ultimate responsibility is to develop the major policies and guidelines to be adopted by schools.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cyber-Safety Policy Elements in the Era of Online Learning: A Content Analysis of Policies in the UAE

Siyam

Hussain

2021

TechTrends

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“…Another possible direction may be considering the application of ML to the different types of cyberbullying (i.e., flaming, harassment, denigration, impersonation, outing/trickery, exclusion, cyberstalking, revenge porn, etc.) included in the most relevant taxonomies [13,14,15,16].  The authors of the selected reviews raised two main technical issues related to online databanks [60,64].…”

Section: E Main Issuesmentioning

confidence: 99%

“…According with these interpretations, cyberbullying is a multidimensional construct encompassing a broad set of online behaviors [12]. Several taxonomies describe flaming, harassment, denigration, impersonation, outing/trickery, exclusion, cyberstalking, revenge porn as forms of cyberbullying [13,14,15,16]. In such a broad categorization, Menesini and colleagues have traced a line between two main forms of cyberbullying: i. written cyberbullying in the form of verbal offenses; ii.…”

Section: Introductionmentioning

confidence: 99%

Cyberbullying Detection Through Machine Learning: Can Technology Help to Prevent Internet Bullying?

Angelis*¹,

Perasso²

2020

IJMH

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Cyberbullying constitutes a threat to adolescents’ psychosocial wellbeing that developed alongside technological progress. Detecting online bullying cases is still an issue because most of victims and bystanders do not timely report cyberbullying episodes to adults. Therefore, automatized technologies may play a critical role in detecting cyberbullying through the use of Machine Learning (ML). ML covers a broad range of techniques that enables systems to quickly access and learn from data, and to make decisions about complex problems. This contribution aims at deepening the role of ML in cyberbullying detection and prevention. Specifically, the following issues are addressed: i. identifying the features most frequently considered to develop ML models predicting cyberbullying; ii. identifying the most used ML algorithms and their evaluation methods; iii. understanding the implication of ML for prevention; iv. highlighting the main theoretical and methodological issues of ML algorithms in predicting cyberbullying. To answer these research questions, a systematic review of literature reviews, from a total of n=186 records from online databanks, has been conducted. Ten literature reviews have been elected to analyze and discuss evidence about ML preventative potential against cyberbullying. Most of the models used content-based features to predict cyberbullying. The majority of these features includes words written in social network posts, whereas Support Vector Machine, Naïve Bayes, and Convolutional Neural Networks are the most used alghorithms. Methodological and technical issues have been critically discussed. ML represents an innovative preventative strategy that may optimize and integrate educational programs for adolescents and be the starting point of the development of technology-based automatized detection strategies. Future research is challenged to develop algorithms capable of detecting cyberbullying from several multimedia sources.

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Examining the Limits of What Schools Can Do to Reduce Peer Cyberbullying

Campbell¹,

Lister²

2021

The Palgrave Handbook of Educational Leadership and Management Discourse

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Developing a cyberbullying conceptual framework for educators

Cited by 31 publications

References 29 publications

Cyber-Safety Policy Elements in the Era of Online Learning: A Content Analysis of Policies in the UAE

Cyber-Safety Policy Elements in the Era of Online Learning: A Content Analysis of Policies in the UAE

Cyberbullying Detection Through Machine Learning: Can Technology Help to Prevent Internet Bullying?

Examining the Limits of What Schools Can Do to Reduce Peer Cyberbullying

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