Fake News Detector
Machine learning web application that classifies news articles as real or fake using Natural Language Processing.
Project Overview
The Fake News Detector is a machine learning-based web application designed to combat misinformation. By utilizing Natural Language Processing (NLP) techniques, the platform processes news text and predicts its authenticity with high accuracy via trained classification models.
The Problem
The rapid spread of misinformation online threatens public discourse. Identifying fake news manually is tedious and scales poorly, necessitating automated systems that can analyze text linguistics to detect deceptive patterns quickly.
The Solution
A comprehensive NLP pipeline that ingests article text, tokenizes and vectorizes the input, and feeds it into a pre-trained machine learning model, returning an instant authenticity probability score to the user.
Architecture & Technology Stack
The core machine learning model is trained on diverse datasets using Scikit-Learn and exported as a serialized model. A lightweight Flask API loads this model to serve real-time predictions to a web interface constructed with vanilla web technologies.
Challenges Faced
The primary challenge was data preprocessing—handling varying text structures, removing stop words, and properly vectorizing textual data to prevent overfitting. Optimizing the Flask API response time when loading the Scikit-learn model into memory was also crucial for a smooth user experience.
Future Improvements
- Upgrading the model from traditional ML to Deep Learning (e.g., BERT or LSTM) for higher accuracy.
- Adding a web scraping feature to analyze live URLs instead of raw text.
- Implementing crowdsourced feedback loops to retrain the model over time.