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Technological solutions for cyberbullying
Abstract
This chapter reviewed the literature regarding technological solutions to prevent and intervene in cyberbullying incidents. Social media companies, Internet service providers, email service providers, instant-messaging apps, apps for cyberbullying, and other technological solutions were examined. We found that (a) parental control and filtering, (b) blocking an account, (c) removing the content, (d) reporting, (e) directing to online resources, and (f) safety centers were the most frequently used solutions to cyberbullying. The available technological solutions were mostly preventative. Moreover, bullying and harassment instead of cyberbullying seemed to be preferred as the technical term by the technology providers. No scientific evidence about effectiveness or ineffectiveness exists for any of the solutions provided by the current technological tools except for the online cyberbullying detection systems, the ReThink software, and a research study analyzing the impact of human face and a video film to enhance empathy for cyberbullying. Along with the recommendations for future research, findings are discussed in terms of their implications for technology-based cyberbullying solutions.
... In an attempt to improve the understanding of cyberbullying further, this study takes a unique approach to understanding cyberbullying, one that differs from well-known and accepted psychological theories by focusing on technical modalities that youths are currently using. In the framework proposed in the Theory of Affordances (Gibson, 1979), cyberbullying can be conceptualized as a technical problem (Topcu-Uzer & Tanrıkulu, 2018). Gibson (1979) emphasized the affordances an object offers and explained that the way the object is used is determined by the relationships between the affordances of the object and the intentions that a user has for the object. ...
... Following their own needs, users decide how to utilize the affordances the object provides. From this point of view, cyberbullying can be explained as a problem stemming from the relationships between the affordances of ICTs and the intentions of ICT users (Topcu-Uzer & Tanrıkulu, 2018). Cyberbullying takes place as ICT users, who often describe themselves as proficient in mastering diverse ICT affordances (Vandebosch & Van Cleemput, 2008), decide to use ICT affordances to bully their victims virtually. ...
This study addressed the adverse effects of cyberbullying on our young people, focusing particularly on immigrant youths. To minimize cyberbullying risks, while maximizing online opportunities, this study explored two types of uses for information and communication technologies (ICT): leisure and information as risk and protective factors. By including two types of ICT use as mediators in adopting a structural equation modeling (SEM) analysis, this study sought to explore the full mechanism governing the ways cyberbullying affects immigrant youths including its indirect paths via two types of ICT uses as well as its direct path. The results demonstrated the need to combat cyberbullying of immigrant youths, providing empirical evidence of its harmful influences on this vulnerable population. The harmful influences of cyberbullying were more pronounced among youths who frequently used ICT for leisure than among those who used it primarily for information. Interestingly, this study identified significant indirect paths for different ICT uses for immigrant students but no significant effects for nonimmigrant students, showing the importance of targeted approaches for immigrant students.
... However, DL models have been shown to achieve state-of-the-art performance in many NLP tasks [25][26][27]. Many M/DL algorithms are implemented to monitor and track the interactions of children and adolescents with M-APPs [15,28,29] as discussed in this concise summary but very little literature is available concerning mobile app classification for securing adolescents. ...
The integration of mobile devices into adolescents’ daily lives is significant, making it imperative to prioritize their safety and security. With the imminent arrival of fast internet (6G), offering increased bandwidth and reduced latency compared to its predecessor (5G), real-time streaming of high-quality video and audio to mobile devices will become feasible. To effectively leverage the fast internet, accurately classifying Mobile Applications (M-APPs) is crucial to shield adolescents from inappropriate content, including violent videos, pornography, hate speech, and cyberbullying. This work introduces an innovative approach utilizing Deep Learning techniques, specifically Attentional Convolutional Neural Networks (A-CNNs), for classifying M-APPs. The goal is to secure adolescent mobile usage by predicting the potential negative impact of M-APPs on adolescents. The proposed methodology employs multiple Machine and Deep Learning (M/DL) models, but A-CNNs based on Bidirectional Encoder Representations from Transformers embeddings outperformed other models, achieving an average accuracy of 88.74% and improving the recall from 99.33% to 99.65%.
... To this end, machine learning algorithms can be used in apps to prevent children from inappropriate apps, such as those involving cyberbullying. Many machine-learning-based apps have also been developed to monitor the interactions of mobile apps with children and adolescents [14]. This interactivity can also be used to understand sustainable behavior and to generate a lead for educators to map proactive interest in the SDGs. ...
Digital media has a massive presence in the modern world, and it significantly impacts kids’ intellectual, cognitive, ethical, and social development. It is nearly impossible to isolate kids from digital media. Therefore, adult content on mobile applications should be avoided by children. Although mobile operating systems provide parental controls, handling such rules is impossible for illiterate people. Consequently, kids may download and use adults’ mobile applications. Mobile applications for adults often publish age group information to distinguish user segments that can be used to automate the downloading process. Sustainable Development Goal (SDG) #4 emphasizes inclusivity and equitability in terms of quality of education and the facilitation of conditions for the promotion of lifelong learning for everyone. The current study can be counted as being in line with SDG#4, as it proposes a machine-learning-based approach to the prediction of the suitability of mobile applications for kids. The approach first leverages natural language processing (NLP) techniques to preprocess user reviews of mobile applications. Second, it performs feature engineering based on the given bag of words (BOW), e.g., abusive words, and constructs a feature vector for each mobile app. Finally, it trains and tests a machine learning algorithm on the given feature vectors. To evaluate the proposed approach, we leverage the 10-fold cross-validation technique. The results of the 10-fold cross-validation indicate that the proposed solution is significant. The average results of the exploited metrics (precision, recall, and F1-score) are 92.76%, 99.33%, and 95.93%, respectively.
... Facebook Bullying Prevention Hub), and information regarding action and response (e.g. platform community guidelines) (Topcu-Uzer and Tanrikulu, 2018). For example, the availability of anonymous reporting resources facilitates individuals reporting cyberbullying without the risk of being exposed (Langos and Giancaspro, 2019) or being further targeted by the cyberbully (Benzmiller, 2013). ...
While cyberbullying has been recognized as a critically important social problem, a void remains regarding the role of digital media. To address this gap, we propose the social-ecological model of cyberbullying, an expanded model that builds on Bronfenbrenner’s ecological systems theory (EST) and expands Swearer and Espelage’s social-ecological model of bullying. A strength of the proposed model is the addition of the digital context as a new ecology in the everyday lives of youth, which is closely interconnected with all the other systems. Furthermore, the model incorporates digital-specific factors within each ecological system of the original EST model. This provides scholars with a holistic model that they can test, finetune, and expand. A practical implication of the model is that it can guide the creation and implementation of effective and age-appropriate cyberbullying prevention and intervention approaches because it considers in the chronosystem life phases, life transitions, historical events, and crises.
Cyberbullying is a specific form of online aggression that has mainly been investigated amongst children, adolescents, and emerging adults. They can be involved in cyberbullying as a victim, perpetrator, and/or bystander. Cyberbullying research has made use of quantitative methods (such as cross-sectional and longitudinal surveys and experiments) as well as qualitative methods (such as interviews and focus groups) in both off- and online settings (e.g. school surveys and online interviews). Apart from methods that rely on respondents’ active involvement, researchers have also employed methods that focus on analysing existing online data that represent cyberbullying instances or people’s accounts thereof (e.g. manual or automatic content analysis and automatic detection). As cyberbullying research often involves young people and deals with transgressive and harmful behaviours, it requires scholars to carefully reflect upon ethical issues (e.g. informed consent and assent, confidentiality, and power relationships).
Cyberbullying has become a prominent risk for youth and an increasing concern for parents. To help parents reduce their child's cyberbullying risk, anti-bullying apps (ABAs)—mobile applications for identifying and preventing instances of cyberbullying—have been developed in recent years. Given that ABAs are an emerging technology, limited research has been conducted to understand the factors predicting parents' intentions to use them. Drawing on three interdisciplinary theoretical frameworks, a sample of parents in the U.S. recruited through Amazon Mechanical Turk completed an online survey to assess parents' knowledge of, attitudes about, and intentions to use ABAs. Participants also rated the importance of a range of ABA functions and provided information about their child's social media use and bullying history. A series of path analyses revealed that the importance parents placed on an app's ability to provide information about their child's cyberbullying risk predicted more positive attitudes toward ABAs and greater perceived usefulness of them. Stronger intentions to use ABAs were predicted by greater cyberbullying concern, greater importance of social recommendations, greater perceived usefulness, more positive attitudes toward the apps, and lower ratings of the importance of ease of use. These findings shed light on the factors predicting parents' intentions to use ABAs and the app features they view as most important. Crucial directions for future research and implications for anti-bullying efforts are discussed.
The emergence of the metaverse, a virtual reality space, has ushered in a new era of digital experiences and interactions in global information systems. With its unique social norms and behaviors, this new world presents exciting opportunities for users to connect, socialize, and explore. However, as people spend more time in the metaverse, it has become increasingly apparent that the issue of cyberbullying needs to be addressed. Cyberbullying is a serious problem that can harm victims psychologically and physically. It involves using technology to harass, intimidate, or humiliate individuals or groups in global information systems. The risk of cyberbullying is high in the metaverse, where users are often anonymous. Therefore, it is crucial to establish a safer and more respectful culture within the metaverse to detect and prevent such incidents from happening.
Cyberbullying has become increasingly prevalent, particularly on social media. There has also been a steady rise in cyberbullying research across a range of disciplines. Much of the empirical work from computer science has focused on developing machine learning models for cyberbullying detection. Whereas machine learning cyberbullying detection models can be improved by drawing on psychological theories and perspectives, there is also tremendous potential for machine learning models to contribute to a better understanding of psychological aspects of cyberbullying. In this paper, we discuss how machine learning models can yield novel insights about the nature and defining characteristics of cyberbullying and how machine learning approaches can be applied to help clinicians, families, and communities reduce cyberbullying. Specifically, we discuss the potential for machine learning models to shed light on the repetitive nature of cyberbullying, the imbalance of power between cyberbullies and their victims, and causal mechanisms that give rise to cyberbullying. We orient our discussion on emerging and future research directions, as well as the practical implications of machine learning cyberbullying detection models.
This chapter intends to present the most common technological solutions that can be implemented to prevent and reduce cyberbullying. Three main questions will be addressed: How can children stay safe online? What can the information technology (IT) industry do to combat cyberbullying? How effective is automatic cyberbullying detection? The chapter will illustrate the progress that has been made to reduce cyberbullying through technological means and discuss the notion of industry self-regulation. Indeed, the IT industry has a responsibility to respect societal obligations towards users, especially when users are children. While many companies in the industry are working responsibly on solutions for the safer use of technology, some global internet service providers are involved in the illicit use of users' personal data. As a consequence, problems of online safety cannot be solved locally, but through concerted actions undertaken at an international level.
This chapter intends to present the most common technological solutions that can be implemented to prevent and reduce cyberbullying. Three main questions will be addressed: How can children stay safe online? What can the information technology (IT) industry do to combat cyberbullying? How effective is automatic cyberbullying detection? The chapter will illustrate the progress that has been made to reduce cyberbullying through technological means and discuss the notion of industry self-regulation. Indeed, the IT industry has a responsibility to respect societal obligations towards users, especially when users are children. While many companies in the industry are working responsibly on solutions for the safer use of technology, some global internet service providers are involved in the illicit use of users' personal data. As a consequence, problems of online safety cannot be solved locally, but through concerted actions undertaken at an international level.
To date, there is a limited amount of research exploring how emerging adults cope with cyberbullying despite being prolific users of technology. The aim of this qualitative study was to explore emerging adults’ views, opinions, and perceptions of how individuals within this developmental period cope with being targeted by cyberbullying behaviors. Six focus groups were conducted with 39 participants (64% female) between 18 and 25 years of age in Melbourne, Australia. Thematic analysis of the data identified several coping strategies that can be employed in response to cyberbullying, which were categorized as problem-focused coping, emotion-focused coping, or cyber-specific technological solutions. In addition to these ways of coping, two notable findings provide insight into the complex and dynamic pattern of perceptions of control and coping style selection and the perceived effectiveness of such coping strategies. This study has implications for targets of cyberbullying, research, and practice. © 2018 Society for the Study of Emerging Adulthood and SAGE Publishing.
Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness” of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance.