Arabic Fake News Detection: A Multi-Model Fusion-Based Multi-Classification Recognition Scheme

This project focuses on Arabic fake news recognition, integrating traditional machine learning, LSTM deep learning, the AraBERT pre-trained model, and the MarBERT+LSTM hybrid architecture to achieve automatic classification and credibility assessment of multi-category news content, providing a complete technical reference scheme for fake news detection in the Arabic NLP field.

Fake news dissemination is a global information governance challenge. Arabic fake news detection faces unique technical challenges: Arabic has complex morphological features, rich dialectal variations, and a right-to-left writing system, so directly applying existing models yields poor results; labeled data for Arabic NLP is scarce, and high-quality pre-trained models are not as abundant as those for English, requiring carefully designed models and training strategies.

The project adopts a multi-model comparison and fusion approach:

1. Traditional Machine Learning: Extract TF-IDF and bag-of-words model features, combined with SVM and random forest to form a baseline, which is more stable when data volume is small;
2. LSTM Deep Neural Network: Captures long-distance dependencies in text, adapting to the complex syntactic structure of Arabic;
3. AraBERT Pre-trained Model: A BERT variant optimized for Arabic, improving semantic understanding accuracy;
4. MarBERT+LSTM Hybrid Architecture: Combines MarBERT's advantages in social media text with LSTM's flexibility in sequence modeling to achieve complementary strengths.

The project uses fine-grained multi-classification (real news, fake news, satirical content, unverified gray area), which is more in line with actual application scenarios; the model's output layer is adjusted to adapt to multi-classification, and evaluation metrics are extended to F1-score and confusion matrix to analyze the recognition performance of each category in detail.

A standard training/validation/test split strategy is adopted, and cross-validation is implemented to reduce random bias; the evaluation focuses on overall accuracy and minority class recall to avoid the model being biased towards the majority class and ensure credible results.

1. Language Feature Adaptation: The uniqueness of Arabic is considered in all links, providing a reference for NLP applications in low-resource languages;
2. Model Fusion Strategy: The hybrid architecture improves accuracy and robustness, suitable for practical deployment;
3. Interpretability Consideration: Enhances system transparency through attention mechanism visualization and feature importance analysis, complying with ethical requirements for content review.

Application scenarios include social media content review, credibility labeling for news aggregation, and government public opinion monitoring; limitations include facing the challenge of adversarial attacks, and cross-domain generalization ability needs to be solved with continuous learning and incremental update mechanisms.

This project demonstrates the effectiveness of the multi-model fusion strategy in low-resource language NLP tasks, provides a complete technical evolution path from traditional machine learning to hybrid architecture, and offers a valuable reference implementation for researchers and engineers in the fields of multilingual NLP, content security, and social media governance.