FactFinder – AI-Powered Fake News Detection Platform

Team: Yash G. Varma, Krishnan P.V., Sahil Brid, Sheshasai Dusa
Achievement: 2nd Position at Hackanova 3.0 TCET (36 hours)

Problem Statement

The proliferation of misinformation poses significant challenges to information integrity in the digital age. FactFinder addresses this by developing a web-based application that leverages machine learning to detect fake news.

Nature of the Solution

FactFinder is a web application designed to assess the authenticity of news articles. Users can input a news article, and the application processes the text using a pre-trained BERT model, which captures contextual nuances in the language. The ensemble model then classifies the article as either "Real" or "Fake."

Technologies Used

• ML Model: Ensemble model combining Bidirectional Encoder Representations from Transformers (BERT) and other classifiers.
• Language: Python
• Backend: Flask
• Frontend: HTML and CSS
• Dataset: WELFake dataset (72,000+ labeled news articles)