A comprehensive framework was developed to detect political bias in Arabic news articles, with a case study focusing on media reporting of the Palestinian issue. The methodology integrates MARBERT contextual embeddings with classical and deep learning classifiers, including SVM, Logistic Regression, Random Forest, and LSTM. The scalability of data processing was ensured through Apache Spark for potential real-time deployment. Experimental results showed that fine-tuned MARBERT embeddings combined with LSTM achieved the highest classification accuracy of 0.87, along with notable improvements in F1-scores across the pro, against, and neutral categories. These findings highlight the effectiveness of domain-specific fine-tuning of transformer models for political bias classification. The study also addressed class imbalance using SMOTE and class weighting strategies, and assessed feature robustness using multiple vectorization techniques.

This paper describes our system submitted to the OSACT7 AdabEval shared task on Arabic politeness detection (TaskA). The task requires classifying Arabic texts into three categories: Polite, Impolite, and Neutral. We systematically explore multiple approaches, progressing from classical machine learning baselines using pre-trained embeddings to fine-tuned transformer models. Our best system leverages MARBERT, a transformer model pre-trained on one billion Arabic tweets, fine-tuned with Focal Loss to handle the significant class imbalance present in the dataset (70% Neutral). We additionally experiment with hybrid approaches combining fine-tuned embeddings with gradient-boosted classifiers and ensemble methods. Our best single model achieves a macro F1 score of 0.84 and an accuracy of 0.90 on the validation set, substantially outperforming classical ML baselines (F1 = 0.42).