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.

Detecting harmful prompts directed at large language models is a critical safety challenge, especially in Arabic where dialectal variation, code-switching, and culturally specific expressions of harm complicate classification. The ArGuard 2026 shared task addresses this problem through two subtasks: binary classification of prompts as safe or unsafe (B1) and fine-grained classification of unsafe prompts into seven harm domains (B2). We participate with a multi-task fine-tuning of MARBERT that shares encoder representations across a binary head and a seven-class granular head. Training combines focal loss with class weights and label smoothing, a hierarchical consistency regularizer that discourages contradictory safe/unsafe and granular predictions, a weighted sampler, and light character-level augmentation. On the official development set the system reaches a binary macro-F1 of 0.9618 and a granular macro-F1 of 1.0000; on the blind test set it obtains binary macro-F1 0.688 and granular macro-F1 0.752. We analyse the substantial development–test gap and discuss limitations of the submitted system.

Detecting hateful content in Arabic memes is challenging due to dialectal variation, sarcasm, cultural references, and the interplay between visual and textual signals. We participate in ArGuard 2026 Track A (Multimodal Hateful Meme Detection) with a multimodal architecture that fuses AraBERT textual representations, ResNet50 visual features, and a lightweight knowledge-graph (KG) ontology vector derived from domain-specific Arabicbinary keywords. The model jointly optimizes (Hateful / Not Hateful) and multi-label finegrained classification through a gated fusion layer with strong regularization (dropout 0.45, weight decay 0.05, label smoothing 0.1). Gradual unfreezing of AraBERT layers, weighted sampling, and Arabic-specific normalization further improve robustness. On the official development set the system reaches a binary macro-F1 of 0.710; on the blind test set it obtains binary macro-F1 0.717. We analyse the contribution of each modality and discuss remaining challenges of fine-grained multi-label prediction under severe class imbalance.