Detection of Offensive Messages in Arabic Social Media Communications

The popularity of social networks, such as Facebook, Twitter and Instagram, has dramatically increased during the last years, especially with the exponential growth in smartphones and mobile devices. This has, in turn, opened the door to numerous cyber threats specifically targeting social media users. Cyberbullying is an example of such threat impacting children, teenagers and young adults. Recently, this threat has also become a significant issue in the Arab world, especially with the wide adoption of social media by the young generation. Unfortunately, most existing research contributions detect cyberbullying in English language. These contributions are not relevant in our context due to the differences in the culture and the environment surrounding the users. This paper proposes a scheme for the detection of cyberbullying in Arabic social media streams. The proposed scheme detects cyberbullying comments based on a corpus of bullying and aggressive keywords. In addition, the bullying comments are classified according to their strength into three classes, namely mild, medium, and strong, using a weighted function. We evaluate the proposed scheme using real dataset, collected from Youtube and Twitter. The experiments show that the proposed scheme can accurately identify most of the bullying comments.