# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work  
For several years, the researchers have worked intensively on cyberbully detection to find a way to control or reduce cyberbully in Social Media platforms. Cyber-bullying is troubling, as victims cannot cope with the emotional burden of violent, intimidating, degrading, and hostile messages. To reduce its harmful effects, the cyberbullying phenomenon needs to be studied in terms of detection, prevention, and mitigation. Presently, there is a range of global initiatives aimed at preventing cyberbully and improving the safety of internet users, including children [14; 15]. In the literature, there are many studies to prevent cyberbully in what is called intervention and prevention approaches. Such approaches originate from the psychology and education fields. However, these approaches are globally rare. Besides, cyberbully victims often refuse to speak with a parent [16], teacher [17], or other adults [18]. They spend much time online [19], tend to get anonymous help [20], and post on the Internet a need for information and assistance [21]. However, the effective way of delivering cyberbullying solutions is through the Internet. Web-based approaches can also be used whenever and wherever the patient prefers [22]. For instance, the University of Turku, Finland, has established an anti-cyberbully program called Kiva [9], and Anti-Harassment campaign in France [10], and an anti-cyberbully initiative by the Belgian government [11]. Ideally, these prevention and intervention approaches should: (1) increase awareness of potential cyberbully threats through individualized intensive intervention strategies based on the victims' needs [23; 24; 25; 26]; (2) provide health education and teach emotional self-management skills [27]; (3) increase awareness of victims in both reactive measures (e.g., deleting, blocking and ignoring messages), and preventive measures (e.g., increased awareness and security) [28]; provide practical strategies and resources that allow victims to cope with experienced stress and negative emotions [28]; (4) aim to reduce traditional bullying as well [29] since victims are often involved in both forms of bullying [30; 31; 32]; and (5) include empathy training, Internet labelling and healthy Internet behavior [33; 34]. Thus far, there has been difficulty in preventing cyberbullying. Most parents and teachers rely on the awareness of children on the causes and impacts of cyberbullying. Some parents think that peer-mentoring is an effective way to prevent cyberbullying, particularly in the teenage years, when peers have a more significant impact than the family and school. Therefore, more specific approaches or online resources need to be developed to help the victims [24]. For example, [PERSON] et al. [35] provided a prevention caveat stating that bully prevention programs produce a minimal change in student behavior [25]. Similarly, authors in [36] suggest that schools should take the following measures in formulating their cyberbullying prevention program: (1) Define cyberbullying; (2) Have strong policies in place; (3) Train staff, students, and parents on policy identify cyberbullying when they see it; and (4) Use internet filtering technologies to ensure compliance. Past research has indicated that social reinforcement may be a dominant protective factor in mitigating the adverse effects of cyberbullying [37; 38]. To get the required reinforcement to minimize the related adverse effects of cyberbullying, they must seek help. However, some reports show that cyberbullying victims are unable to report bullying cases and prefer to be silent [6; 39]. Some teenagers rarely seek assistance from their teachers or school advisors [40; 41]. Based on the above issues of prevention approaches, the need to detect and filter cyberbullying on social media is highly needed. Thus, this section is dedicated to inspecting cyberbully detection techniques.