# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 1 Introduction  
Due to the significant development of Internet 2.0 technology, social media sites such as Twitter and Facebook have become popular and play a significant role in transforming human life [1; 2]. In particular, social media networks have incorporated daily activities, such as education, business, entertainment, and e-government, into human life. According to [3], social networking impacts are projected to exceed 3.02 billion active social media users each month globally by 2021. This number will account for approximately one-third of the Earth's population. Moreover, among the numerous existing social networks, Twitter is a critical platform and a vital data source for researchers. Twitter is a popular public microblogging network operating in real-time, in which news often appears before it appears in official sources. Characterized by its short message limit (now 280 characters) and unfiltered feed, Twitter use has rapidly increased, with an average of 500 million tweets posted daily, particularly during events [3]. Currently, social media is an integral element of daily life. Undoubtedly, however, young people's usage of technology, including social media, may expose them to many behavioral and psychological risks. One of these risks is cyberbullying, which is an influential social attack occurring on social media platforms. In addition, cyberbullying has been associated with adversemental health effects, including depression, anxiety, and other types of self-harm, suicidal thoughts, attempted suicide, and social and emotional difficulties [4; 5; 6; 7]. Furthermore, the substantial increase in the number of cyberbullying cases has highlighted the danger of cyberbullying, particularly among children and adolescents, who can be inconsiderate and juvenile. Children and adolescents take bullying seriously without understanding how to manage social issues; this leads them to express their emotions on social media in a manner that can hurt others. According to [8], several studies have shown that bullies often suffer from psychological conditions, leading them to bully and inflict suffering on others. Thus, cyberbullying is similar to an epidemic, and can lead to an aggressive society, particularly regarding high-tech university and school students. Therefore, many global initiatives have been proposed to tackle the issue of cyberbullying. These initiatives aim to enhance the safety of Internet users, particularly children; for example, the University of Turku, Finland, established an anti-cyberbullying program called Kiva [9], an anti-harassment campaign took place in France [10], and an anti-cyberbully initiative was established by the Belgian government [11]. However, because the Internet's content is vast and difficult to control, detecting and filtering cyberbullies is considered complementary to the legalization and intervention approaches. Thus, detecting cyberbullying in social media is necessary and should be paid high attention so that children and society are protected from its side-effects. Cyberbullying is now a research topic, with researchers aiming to detect, control, and reduce cyberbullying in social media. One direction in this field is to detect a user's intention to post offensive content by analyzing offensive language based on different features, such as the structure and unique content, in addition to the users' writing style. Another direction of cyberbullying research is to detect text content using machine learning for offensive language detection and classification. Our focus in this comparative study is to review the machine learning classifiers used in the detection of cyberbullying, and examine the performance of these classifiers using accuracy, precision, recall, and F1 score as performance metrics. Therefore, the main contributions of this work are:  
* We conducted an extensive review of quality papers to determine the machine learning (ML) methods widely used in the detection of cyberbullying in social media (SM) platforms.