Machine Learning Model for Offensive Speech Detection in Online Social Networks Slang Content

The majority of the world’s population (about 4 billion people) now uses social media such as Facebook, Twitter, Instagram, and others. Social media has evolved into a vital form of communication, allowing individuals to interact with each other and share their knowledge and experiences. On the other hand, social media can be a source of malevolent conduct. In fact, nasty and criminal activity, such as cyberbullying and threatening, has grown increasingly common on social media, particularly among those who use Arabic. Detecting such behavior, however, is a difficult endeavor since it involves natural language, particularly Arabic, which is grammatically and syntactically rich and fruitful. Furthermore, social network users frequently employ Arabic slang and fail to correct obvious grammatical norms, making automatic recognition of bullying difficult. Meanwhile, only a few research studies in Arabic have addressed this issue. The goal of this study is to develop a method for recognizing and detecting Arabic slang offensive speech in Online Social Networks (OSNs). As a result, we propose an effective strategy based on the combination of Artificial Intelligence and statistical approach due to the difficulty of setting linguistic or semantic rules for modeling Arabic slang due to the absence of grammatical rules. An experimental study comparing frequent machine learning tools shows that Random Forest (RF) outperforms others in terms of precision (90%), recall (90%), and f1-score (90%).