Effectiveness of Cyberbullying Prevention Strategies in the UAE

Nuaimi, Abdulla Al

doi:10.1007/978-981-15-8354-4_72

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…To summarize, and per our second goal, we propose that future research take into consideration and completely disclose a set of essential information in order to enhance the quality of future datasets, models, and the performance of classifiers. It is critical to give annotators clear instructions based on the characteristics that define cyberbullying (i.e., intentionality, repetition, aggression, and peer conduct), as well as to guarantee that the annotators are specialists in the area of cyberbullying [51][52][53][54]. Furthermore, data extraction from users should be acquired through peers, and users' privacy should be prioritized during this process [55].…”

Section: Discussionmentioning

confidence: 99%

“…The definition of the emotional component of the text is used to study various forms and manifestations of online aggressiveness: trolling [38,45], verbal hostility [39,[46][47][48], cyberbullying through mobile applications [34,41,49], various types of manipulation [50][51][52], inciting discord [53], etc. The network component of such a form of offline aggression as mass protest is the subject of a complex method of cybermetry, or cybermetric analysis [54][55][56].…”

Section: Machine Learningmentioning

confidence: 99%

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A Review of Machine Learning Techniques in Cyberbullying Detection

Sultan¹,

Omarov²,

Kozhamkulova³

et al. 2023

Computers, Materials &Amp; Continua

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Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents' lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review of 13 papers from four scientific databases. The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing. In this review, we consider a cyberbullying detection framework on social media platforms, which includes data collection, data processing, feature selection, feature extraction, and the application of machine learning to classify whether texts contain cyberbullying or not. This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon's description and depiction, allowing future solutions to be more practical and effective.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

A Review of Machine Learning Techniques in Cyberbullying Detection

Sultan¹,

Omarov²,

Kozhamkulova³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…However, text-based cyberbullying is the most popular form used by misbehaving users. Due to the enormous amount, variety, and velocity of data and information generated by users, manual detection and/or relying on user reports are not an efficient or effective way to overthrow cyberbullying [2,[7][8][9][10][11][12]. Accordingly, automatic cyberbullying detection is important to defend the cyber realm and protect communities and individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic detection of cyberbullying became the subject of many studies over the past few years [1,4,5,9,13,14]. However, cyberbullying has dramatically increased recently [2,15].…”

Section: Introductionmentioning

confidence: 99%

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Alhashmi¹,

Darem²

2022

Computer Systems Science and Engineering

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Due to the proliferation of internet-enabled smartphones, many people, particularly young people in Arabic society, have widely adopted social media platforms as a primary means of communication, interaction and friendship making. The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world. However, such networks expose young people to cyberbullying and offensive content that puts their safety and emotional well-being at serious risk. Although, many solutions have been proposed to automatically detect cyberbullying, most of the existing solutions have been designed for English speaking consumers. The morphologically rich languages-such as the Arabic language-lead to data sparsity problems. Thus, render solutions developed for another language are ineffective once applied to the Arabic language content. To this end, this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model. A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected from five different platforms. The outputs of the selected classifiers are combined using consensus-based decisionmaking in which the F1-Score of each classifier was used to rank the classifiers. Then, the Sigmoid function, which can reproduce human-like decision making, is used to infer the final decision. The outcomes show the efficacy of the proposed model comparing to the other studied classifiers. The overall improvement gained by the proposed model reaches 1.3% comparing with the best trained classifier. Besides its effectiveness for Arabic language content, the proposed model can be generalized to improve cyberbullying detection in other languages.

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