| Title | Bullying Hurts: A Survey on Non-Supervised Techniques for Cyber-Bullying Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Farag, Nadine, El-Seoud, Samir Abou, McKee, Gerard, Hassan, Ghada |
| Conference Name | Proceedings of the 2019 8th International Conference on Software and Information Engineering |
| Publisher | Association for Computing Machinery |
| Conference Location | Cairo, Egypt |
| ISBN Number | 978-1-4503-6105-7 |
| Keywords | Deep Learning, Human Behavior, human factors, Metrics, pubcrawl, stylometry, supervised learning, unsupervised learning |
| Abstract | The contemporary period is scarred by the predominant place of social media in everyday life. Despite social media being a useful tool for communication and social gathering it also offers opportunities for harmful criminal activities. One of these activities is cyber-bullying enabled through the abuse and mistreatment of the internet as a means of bullying others virtually. As a way of minimising this occurrence, research into computer-based researched is carried out to detect cyber-bullying by the scientific research community. An extensive literature search shows that supervised learning techniques are the most commonly used methods for cyber-bullying detection. However, some non-supervised techniques and other approaches have proven to be effective towards cyber-bullying detection. This paper, therefore, surveys recent research on non-supervised techniques and offers some suggestions for future research in textual-based cyber-bullying detection including detecting roles, detecting emotional state, automated annotation and stylometric methods. |
| DOI | 10.1145/3328833.3328869 |
| Citation Key | farag_bullying_2019 |
Bullying Hurts: A Survey on Non-Supervised Techniques for Cyber-Bullying Detection