Hybrid Feature and Optimized Deep Learning Model Fusion for Detecting Hateful Arabic Content

Detecting hate speech in Arabic social media content is critical for ensuring safe, inclusive,
and respectful online communication. However, this task remains challenging due to Arabic’s morphological
richness, dialectal variations such as Levantine, and the scarcity of high-quality annotated data. This study
proposes a comprehensive and language-aware approach to Arabic hate speech detection that integrates
advanced preprocessing, targeted data augmentation, hybrid feature extraction, and deep ensemble learning.
Our experiments are conducted on a Levantine Arabic tweet dataset labeled hateful or non-hateful. To
address lexical variability and noise common in user-generated content, we apply a dedicated preprocessing
pipeline that includes normalization, diacritic removal, and emoji filtering. To further enhance generalization
and mitigate data imbalance, we employ two augmentation strategies: synonym replacement using a
curated Arabic lexicon and semantic-preserving back-translation through English. We investigate lexical
and contextual approaches for feature extraction, including TF-IDF vectors, contextualized AraBERT
embeddings, and a hybrid combination of both. These features are input into multiple deep learning
classifiers, including CNN-BiGRU, BiLSTM, and DNN architectures. To maximize predictive performance,
we develop an ensemble framework that integrates these models. The final prediction is obtained through
a weighted fusion of individual model outputs, where the optimal weights are selected using the Grey
Wolf Optimizer (GWO), aiming to maximize classification accuracy. Experimental results demonstrate that
our proposed hybrid and ensemble-based architecture achieves superior performance, with an accuracy of
83.33% and a ROC-AUC score of 89.5%, outperforming individual models and conventional baselines.
These findings highlight the effectiveness of hybrid feature representations and nature-inspired optimization
in enhancing Arabic hate speech detection. Our approach offers a scalable, linguistically informed solution
for robust content moderation in Arabic digital spaces.