A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

Haidar, Batoul; Chamoun, Maroun; Serhrouchni, Ahmed

doi:10.25046/aj020634

Cited by 90 publications

(51 citation statements)

References 39 publications

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“…In addition to the previous behaviors, cyberbullying is another serious issue that has been addressed by many researchers in English language. For the Arabic language, Haidar et al [64] made the first attempt to detect cyberbullying in Arabic language. Their work was the first step into this area, since it needs a lot of enhancements such as considering more features related to cyberbullying and choosing better feature representation.…”

Section: Arabic Anti-social Behaviour Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

114

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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Section: Arabic Anti-social Behaviour Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

114

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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%

“…Haidar et al in [17], suggested a system for detecting cyberbullying in English and Arabic text. The only features included in the first stage were text (content of tweet) and language (English, Arabic).…”

Section: 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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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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“…Moving on to Di Capua et al [9], they proposed a new way for cyberbullying detection by adopting an unsupervised approach, they used the classifiers inconsistently over their dataset, applying SVM on FormSpring and achieving 67% on recall, applying GHSOM on YouTube and achieving 60% precision, 69% accuracy and 94% recall, applying Naïve Bayes on Twitter and achieving 67% accuracy. Additionally, Haidar et al [10] proposed a model to detect cyberbullying but using Arabic language they used Naïve Bayes and achieved 90.85% precision and SVM achieved 94.1% as precision but they have high rate of false positive also the are work on Arabic language.…”

Section: Related Workmentioning

confidence: 99%

Social Media Cyberbullying Detection using Machine Learning

Hani¹,

Mohamed²,

Mostafa³

et al. 2019

IJACSA

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With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. Social networks provides a rich environment for bullies to uses these networks as vulnerable to attacks against victims. Given the consequences of cyberbullying on victims, it is necessary to find suitable actions to detect and prevent it. Machine learning can be helpful to detect language patterns of the bullies and hence can generate a model to automatically detect cyberbullying actions. This paper proposes a supervised machine learning approach for detecting and preventing cyberbullying. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed approach on cyberbullying dataset shows that Neural Network performs better and achieves accuracy of 92.8% and SVM achieves 90.3. Also, NN outperforms other classifiers of similar work on the same dataset.

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A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

Cited by 90 publications

References 39 publications

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Text Mining Techniques for Cyberbullying Detection: State of the Art

Social Media Cyberbullying Detection using Machine Learning

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