Misinformation spreads faster than facts on social media. Fake news can influence public opinion, impact elections, and cause panic.
Our Fake News Detection System takes a news article, headline, or social media post as input and classifies it as Real or Fake. It uses Natural Language Processing (NLP) and machine learning models (Logistic Regression, LSTM, and BERT) to analyze text and make predictions. The system can be deployed as a web app for easy access.
Data Collection: Kaggle Fake News Dataset, LIAR dataset, and News API.
Preprocessing: Tokenization, stopword removal, lemmatization with NLTK and SpaCy.
Feature Engineering: TF-IDF, Word2Vec, and BERT embeddings.
Model Training: Logistic Regression (baseline), LSTM for sequence learning, and BERT for transformer-based classification.
Deployment: Flask backend, simple web UI, and cloud hosting for accessibility.
Handling imbalanced datasets.
Keeping models updated with evolving fake news.
Achieving both accuracy and speed in classification.
Integrating NLP pipelines into a user-friendly interface.
Successfully trained models that achieved high accuracy (>90% with BERT).
Built a working prototype web app for real-time classification.
Designed a scalable architecture for deployment.
Importance of data quality and bias handling in AI systems.
How transformer models like BERT outperform traditional ML methods for text classification.
Balancing accuracy with real-time performance in deployed AI systems.
Team collaboration and agile problem solving in a hackathon setting.
Multi-lingual Support: Extend detection to regional and global languages, since misinformation is not limited to English.
Explainable AI: Add features to show why an article was classified as fake or real, improving transparency and trust.
Integration with Platforms: Provide APIs or browser extensions that can integrate with social media and news sites for real-time verification.
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