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The Fake News Detection System is an AI-powered platform that classifies news articles and videos as real or fake. It allows users to input text or URLs, scrapes the content using BeautifulSoup, and p
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描述
Project Description: Fake News Detection System
This AI-driven Fake News Detection System is designed to verify the authenticity of news articles and videos. It leverages a Multinomial Naive Bayes model trained on labeled datasets to classify input as real or fake. The system supports both text and URL-based input, using BeautifulSoup for content scraping and Scikit-learn for processing and prediction. Additionally, it detects AI-generated videos to prevent the spread of deepfakes. The backend is built using Node.js, Express, and MongoDB, while the frontend leverages React, Vite, and Tailwind CSS for a dynamic user interface. A unique feature includes a browser extension that scans live news feeds in real-time and sends notifications if the content is likely fake. This end-to-end solution aims to combat misinformation and promote media literacy.
本次黑客松进展
During the hackathon, our team successfully built the core functionality of the Fake News Detection System. We integrated a Multinomial Naive Bayes model for text-based news verification and implemented a responsive MERN-based frontend for user interaction. A functional Flask-Node bridge was developed to connect AI predictions with the React interface. Additionally, we introduced real-time video classification to detect AI-generated or fake visuals and built a working prototype of a browser extension that sends live notifications on questionable news sources. The system was tested with real-world data and showed promising results in early-stage evaluations.
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融资状态
We are currently in the early stages of fundraising for the Fake News Detection System. Our aim is to secure initial seed funding to support infrastructure scaling, advanced AI model development, and public deployment. The funds will be used to enhance the system’s capabilities, including real-time video analysis, browser extension expansion, and multilingual support. We are actively seeking support from impact-driven investors, government initiatives, and innovation grants focused on combating misinformation. A detailed pitch deck and demo are available upon request.