Arabic Cyberbullying Detection: Using Deep Learning

Haidar, Batoul; Chamoun, Maroun; Serhrouchni, Ahmed

doi:10.1109/iccce.2018.8539303

Cited by 42 publications

(22 citation statements)

References 20 publications

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“…Authors in [16], authors represented the first study in utilizing deep learning in Arabic cyberbullying detection .They utilized the same dataset in [17], with little changes. Changes include removing all hyperlinks, un-Arabic characters and emoticons.…”

Section: Detection In Arabic Languagementioning

confidence: 99%

“…For Cyberbullying detection in Arabic language, the findings indicate that, SVM classifier is the most used classifier in the classification of Arabic text [6], [17]. Also, most of studies used Twitter platform as a source to collect the datasets [16], [17], [18]. For the surveyed papers, there was no unified dataset, and the maximum size of the dataset is 35273 tweets built by the writers in a study [17],and this size is small compared to the English dataset available.…”

Section: Cyberbullying Detection In Arabic Languagementioning

confidence: 99%

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Text Mining Techniques for Cyberbullying Detection: State of the Art

Bayari¹

2021

Adv. sci. technol. eng. syst. j.

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The dramatic growth of social media during the last years has been associated with the emergence of a new bullying types. Platforms such as Facebook, Twitter, YouTube, and others are now privileged ways to disseminate all kinds of information. Indeed, communicating through social media without revealing the real identity has emerged an ideal atmosphere for cyberbullying, where people can pour out their hatred. Therefore, become very urgent to find automated methods to detect cyberbullying through text mining techniques. So, many researchers have recently investigated various approaches, and the number of scientific studies about this topic is growing very rapidly. Nonetheless, the methods are used to classify the phenomenon and evaluation methods are still under discussion. Subsequently, comparing the results between the studies and identifying their performance is still difficult. Therefore, the current systematic review has been conducted with the aim of survey the researches and studies that have been conducted so far by the research community in the topic of cyberbullying classification based on text language. In order to direct future studies on the topic to a more consistent and compatible perspective on recent works, we undertook a deep review of evaluation methods, features, dataset size, language, and dataset source of the latest research in this field. We made a choice to focus more on techniques that adopted neural networks and machine learning algorithms. After conducting systematic searches and applying the inclusion criteria, 16 different studies were included. It was found that the best accuracy was achieved when a deep learning approach is used particularly CNN approach. It was found also that, SVM is the most common classifier in both Arabic and Latin languages and outperformed the other classifiers. Also, the most widely used feature is N-Gram especially bigram and trigram. Furthermore, results show that Twitter is the main source for the collected datasets, and there are no unified datasets. There is also a shortage of studies in Arabic texts for cyberbullying identification in contrast with English texts.

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Section: Detection In Arabic Languagementioning

confidence: 99%

Section: Cyberbullying Detection In Arabic Languagementioning

confidence: 99%

Text Mining Techniques for Cyberbullying Detection: State of the Art

Bayari¹

2021

Adv. sci. technol. eng. syst. j.

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“…Haider et al [72] used a feedforward neural network to detect Arabic cyberbullying, using tweets as the data set. The authors changed different parameters in the neural network to detect changes and achieve better accuracy.…”

Section: Cybercrime Detection Using Deep Learningmentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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Cybercrimes describe cases of indictable offences and misdemeanours in which computer or any communication tools are involved as targets, commission instruments, incidental to, or that cases are associated with the prevalence of computer technology. Common forms of cybercrimes could be child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing and other cyber hacking. These kinds of cybercrimes are mostly leading to breaching users' privacy, security violation, business loss, financial fraud, or damage in public as well as government properties. Hence, this paper intensively reviews cybercrime detection and prevention methods. It first explores the different types of cybercrimes then discusses their threats against privacy and security in computer systems. It also describes the strategies that cybercriminals might utilize in committing these crimes against individuals, organizations, and societies. The paper then reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyses the vulnerabilities of each technique. As a future study, the paper provides recommendations for the development of cybercrime detection model in which it is capable to effectively detect cybercrime in comparison to the existing techniques.

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“…The writers in [11] applied a neural network with 4 hidden layers for cyberbullying detection. The writers in [12] have been influenced by deep learning approaches using a hybrid CNN-LSTM , a simple CNN, and a mixture of DNN, LSTM and RNN.…”

Section: Deep Learningmentioning

confidence: 99%

A Review on Non-Supervised Approaches for Cyberbullying Detection

2020

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The current period is dominated by social networks in daily life. Despite the several efforts and research practices done to overcome the issue, it still presents a problem. Despite the fact that social networks are useful for social gathering and communication, they also present new opportunities for harmful criminal acts. Cyber-harassment is an example that is enabled through the mistreatment and abuse of the internet as a means of harassing or bullying others virtually. To minimize these occurrences, research into computer-based methods has been per-formed to detect cyber-harassment. This literature survey shows that supervised learning methods were mostly used for cyber-bullying detection. Moreover, some non-supervised methods and other techniques have also shown to be effective in terms of accuracy towards cyber-bullying detection. This paper, therefore, surveys existing recent research on non-supervised techniques as well as it summarizes accuracy results obtained from several papers to discuss the significance of non-supervised learning approaches in comparison with traditional learning methods.

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Arabic Cyberbullying Detection: Using Deep Learning

Cited by 42 publications

References 20 publications

Text Mining Techniques for Cyberbullying Detection: State of the Art

Text Mining Techniques for Cyberbullying Detection: State of the Art

Comprehensive Review of Cybercrime Detection Techniques

A Review on Non-Supervised Approaches for Cyberbullying Detection

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