Fake News Detection

This NLP specialization project offers a complete fake news detection system. The pipeline covers exploratory analysis, preprocessing, baseline models (Naive Bayes, Logistic Regression, XGBoost), transformer model fine-tuning (BERT, RoBERTa, DeBERTa), evaluation on an external dataset (out-of-distribution generalization), and interpretability analysis with SHAP and LIME. The LIAR dataset contains 12,800 political statements labeled by PolitiFact fact-checkers. The project also includes an ethical bias analysis.

Challenges

• Multi-class classification of political statements with contextual nuances
• Model generalization on unseen external datasets
• Model decision interpretability to ensure trust
• Detection and analysis of prediction biases

Solutions

• Progressive pipeline: TF-IDF baseline → BERT/RoBERTa/DeBERTa fine-tuning
• Cross-dataset evaluation to measure out-of-distribution robustness
• SHAP and LIME analysis for prediction explainability
• Ethical bias audit integrated in the evaluation pipeline

Results

• 5 notebooks covering the complete EDA → interpretability pipeline
• Comparison of 6+ models (baseline + transformers)
• SHAP/LIME interpretability analysis on predictions
• Generalization evaluation on external dataset

Technologies

Python · PyTorch · BERT · Transformers · SHAP · LIME · Scikit-learn · XGBoost