Fake News Detection with LSTM
Overview
Developed an advanced fake news detection system using LSTM-based sequence modeling for contextual understanding of news articles. The system leverages pretrained GloVe word embeddings for semantic representation and achieves high performance in distinguishing between authentic and fake news.
Key Features
- LSTM Architecture: Sequential modeling for contextual understanding
- Semantic Embeddings: Pretrained GloVe word embeddings for rich semantic representation
- High Performance: 89% F1-Score on fake news detection
- Text Processing: Advanced preprocessing and feature extraction pipeline
Technical Implementation
Model Architecture
- LSTM Networks: Long Short-Term Memory networks for sequence modeling
- Embedding Layer: GloVe pretrained embeddings for word representation
- Classification Head: Dense layers for binary classification (fake/real)
Data Processing
- Text Preprocessing: Tokenization, cleaning, and normalization
- Sequence Modeling: Variable-length text handling with padding/truncation
- Feature Engineering: Extraction of linguistic and semantic features
Technologies Used
- Deep Learning: LSTM networks for sequence processing
- NLP: Natural Language Processing techniques
- Embeddings: GloVe pretrained word vectors
- Text Processing: Advanced text preprocessing pipeline
- Sequence Modeling: Handling variable-length text sequences
Performance Metrics
- F1-Score: 89%
- Precision: High precision in fake news identification
- Recall: Effective detection of fake news instances
- Robustness: Consistent performance across different news domains
Dataset and Evaluation
- Training Data: Large-scale news article dataset with fake/real labels
- Validation: Cross-validation and holdout testing
- Metrics: F1-score, precision, recall, and accuracy evaluation
Applications
- Media Verification: Automated fact-checking systems
- Social Media Monitoring: Detection of misinformation spread
- News Aggregation: Quality control for news platforms
- Research Tools: Academic research on misinformation
Impact
This project addresses the critical issue of misinformation by providing an automated system for fake news detection, contributing to information integrity and helping combat the spread of false information in digital media.
Satyam Singh
Machine Learning Researcher | Neural Networks & Applied AI
Machine learning researcher focused on building efficient neural network architectures for real-world applications, spanning deep learning, computer vision, and NLP.