Building Credible AI: Detecting Misinformation in Arabic Social Media
A system for Arabic credibility detection on social media content using transformer-based models for accurate classification.
Overview
This research presents a comprehensive system for detecting credibility in Arabic social media content using state-of-the-art transformer-based models.
The Challenge
Social media platforms are flooded with misinformation, and Arabic content presents unique challenges due to its morphological complexity and dialectal variations. Traditional NLP approaches often fail to capture the nuanced patterns that indicate credible vs. non-credible content.
Our Approach
We developed a multi-stage pipeline that:
- Preprocessing: Handles Arabic-specific normalization, including diacritics removal, letter normalization, and emoji handling
- Feature Extraction: Leverages AraBERT embeddings to capture contextual meaning
- Classification: Fine-tuned transformer models for binary credibility classification
Key Results
- Achieved 89.3% accuracy on the benchmark dataset
- Outperformed traditional ML baselines by 15%
- Robust performance across different Arabic dialects
Technical Stack
- PyTorch for model implementation
- Hugging Face Transformers for AraBERT
- FastAPI for deployment
- Docker + Kubernetes for scalability
Conclusion
Transformer-based approaches show significant promise for Arabic NLP tasks, particularly in the critical domain of misinformation detection.
Written by
Ahmad Hussein
AI Systems Architect specializing in LLM orchestration, RAG systems, and scalable cloud platforms. Building secure AI solutions for Fintech, Robotics, and Legal-tech.