Protecting Intellectual Security Through Hate Speech Detection Using an Artificial Intelligence Approach
Sadeem Hamad Alrasheed, Suliman Aladhadh, Abdulatif Alabdulatif · Algorithms · 2025
Online social networks (OSNs) have become an integral part of daily life, with platforms such as X (formerly Twitter) being among the most popular in the Middle East. However, X faces the problem of widespread hate speech aimed at spreading hostility between communities, especially among Arabic-speaking users. This problem is exacerbated by the lack of effective tools for processing Arabic content and the complexity of the Arabic language, including its diverse grammar and dialects. This study developed a two-layer framework to detect and classify Arabic hate speech using machine learning and deep learning with various features and word embedding techniques. A large dataset of Arabic tweets was collected using the X API. The first layer of the framework focused on detecting hate speech, while the second layer classified it into religious, social, or political hate speech. Convolutional neural networks (CNN) outperformed other models, achieving an accuracy of 92% in hate speech detection and 93% in classification. These results highlight the framework’s effectiveness in addressing Arabic language complexities and improving content monitoring tools, thereby contributing to intellectual security and fostering a safer digital space.