Stance Detection in Arabic Tweets: A Machine Learning Framework for Identifying Extremist Discourse

Terrorism remains a critical global challenge, and the proliferation of social media has created new avenues for monitoring extremist discourse. This study investigates stance detection as a method to identify Arabic tweets expressing support for or opposition to specific organizations associated with extremist activities, using Hezbollah as a case study. Thousands of relevant Arabic tweets were collected and manually annotated by expert annotators. After extensive preprocessing and feature extraction using term frequency–inverse document frequency (tf-idf), we implemented traditional machine learning (ML) classifiers—Support Vector Machines (SVMs) with multiple kernels, Multinomial Naïve Bayes, and Weighted K-Nearest Neighbors. ML models were selected over deep learning (DL) approaches due to (1) limited annotated Arabic data availability for effective DL training; (2) computational efficiency for resource-constrained environments; and (3) the critical need for interpretability in counterterrorism applications. While interpretability is not a core focus of this work, the use of traditional ML models (rather than DL) makes the system inherently more transparent and readily adaptable for future integration of interpretability techniques. Comparative experiments using FastText word embeddings and tf-idf with supervised classifiers revealed superior performance with the latter approach. Our best result achieved a macro F-score of 78.62% using SVMs with the RBF kernel, demonstrating that interpretable ML frameworks offer a viable and resource-efficient approach for monitoring extremist discourse in Arabic social media. These findings highlight the potential of such frameworks to support scalable and explainable counterterrorism tools in low-resource linguistic settings.