Technological solutions for cyberbullying

Topcu-Uzer, Cigdem; Tanrıkulu, İbrahim

doi:10.1016/b978-0-12-811423-0.00003-1

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…Although these apps have yet to be widely adopted by parents across broader segments of the population—in large part due to the incipient stages of the technology—at least 16 of these apps were available for consumer use as of 2018 [ 23 ]. Surprisingly, there has been little research to better understand parents' decisions to adopt these types of anti-bullying technologies.…”

Section: Introductionmentioning

confidence: 99%

U.S. parents' intentions to use anti-bullying apps: Insights from a comprehensive model

Wheeler,

Baumel,

Hall

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

U.S. parents' intentions to use anti-bullying apps: Insights from a comprehensive model

Wheeler,

Baumel,

Hall

et al. 2023

Heliyon

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“…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.…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Suitability Prediction for Mobile Applications for Kids

Meng

Malik

et al. 2022

Sustainability

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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.

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Researching Cyberbullying: A Colourful Palette of Methods and Experiences

Vandebosch,

Pabian

2023

Fieldwork Experiences in Criminology and Security Studies

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Technological solutions for cyberbullying

Cited by 10 publications

References 22 publications

U.S. parents' intentions to use anti-bullying apps: Insights from a comprehensive model

U.S. parents' intentions to use anti-bullying apps: Insights from a comprehensive model

Machine-Learning-Based Suitability Prediction for Mobile Applications for Kids

Researching Cyberbullying: A Colourful Palette of Methods and Experiences

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