Fake News Detection App - Web-based NLP & ML Model Integration

Project Overview

The Fake News Detection App is a web-based platform designed to help users identify fake news articles using advanced Natural Language Processing (NLP) and Machine Learning (ML) models. The frontend of the app plays a critical role in ensuring an engaging and user-friendly experience by providing a smooth interface for input, real-time feedback, and easy navigation between the various app features.

Key Features

• Chatbot Interface: A ChatGPT-style interface where users can interact with the app, submit news articles, and receive instant feedback on whether the article is authentic or fabricated.
• Real-time Fake News Detection: Integrates with a backend ML model that uses NLP techniques for binary classification of news articles, distinguishing between legitimate and fake news.
• Chat History: Allows users to view their previous interactions and feedback by storing chat history locally, making it easy to reference past inputs and results.
• Intuitive UI: Clean and simple interface that ensures an easy user experience, guiding users from submitting articles to receiving results and feedback.

Technical Approach

The frontend is built with React, providing a dynamic and responsive UI. The ChatGPT-style chatbot interface interacts with the backend in real-time, sending requests to the model for fake news detection. Local storage is utilized to save user interactions, chat history, and feedback, ensuring a personalized and fluid user experience.

Key components include:

• Frontend Development: The core user interface was developed with React, allowing for a smooth, chat-like experience.
• API Integration: Integrated API calls to send news articles to the backend for NLP-based classification.
• State Management: Used state management techniques to handle real-time data, chat history, and user feedback.
• User-Centric Design: Focused on creating an easy-to-use, responsive UI with clear feedback for every action the user takes.

Development Process

The development followed an agile approach, starting with the design of the user interface and progressively integrating the backend machine learning model. We began by building out the ChatGPT-style chatbot interface, ensuring that users could easily interact with the system. Afterward, we integrated the fake news detection backend, connecting the frontend with the machine learning model via API calls. The application was then tested extensively to ensure smooth interaction, accurate results, and consistent data handling.