erKrishna26/Amharic-language-FakeNews-detection

Comparative Analysis of BiLSTM and AfriBERTa for Fake News Detection in Low-Resource Amharic Language # Comparative Analysis of BiLSTM and AfriBERTa for Fake News Detection in Low-Resource Amharic Language This repository contains the implementation, analysis, and documentation for the project **“Comparative Analysis of BiLSTM and AfriBERTa for Fake News Detection in Low-Resource Amharic Language.”** The work compares a tuned BiLSTM architecture with the transformer-based AfriBERTa model for binary fake news detection in the Amharic language. --- ## 1. Project Overview ### Objective To develop and evaluate deep learning models capable of detecting fake news in the Amharic language, addressing the lack of robust NLP solutions for low-resource linguistic settings. ### Models Examined - **BiLSTM (Optimized Configuration)** Custom architecture tuned for enhanced generalization and reduced overfitting. - **AfriBERTa** Transformer-based model adapted from XLM-RoBERTa and fine-tuned for Amharic fake news classification. ### Core Techniques - Text preprocessing: normalization, cleaning, tokenization, and padding (308-token sequence length) - Dataset balancing via SMOTE - Training stabilization using early stopping, learning rate scheduling, and L2 regularization - Evaluation metrics: Accuracy, Precision, Recall, and F1-Score --- ## 2. Dataset **Source** Hailu, M. (2024). *Amharic Fake News Detection Dataset.* ### Dataset Statistics - Total samples: 8,630 - Fake: 4,185 - Real: 4,445 - Train/Validation/Test split: 70% / 15% / 15% - Padding length: 308 tokens (95th percentile coverage) --- ## 3. Repository Structure | File | Description | |------|-------------| | `Amharic_FakeNews_BiLSTM_AfriBERTa.ipynb` | Implementation of both models | | `REPORT_Comparative_Analysis.pdf` | Complete technical research report | | `PPT_Comparative_Analysis.pdf` | Presentation slides | | `README.md` | Documentation file | --- ## 4. Model Performance | Model | Accuracy | Precision | Recall | F1-Score | Train–Val Gap | |-------|----------|-----------|--------|----------|----------- …