Abstract / Introduction

In today's digital era, fake news has become a major problem as it spreads misinformation and influences public opinion. This project aims to design and implement an AI model that can automatically detect whether a news article or headline is real or fake. The system will analyze the language patterns, word frequency, and context using Natural Language Processing (NLP) techniques and apply machine learning algorithms to classify the content.

By training on publicly available fake news datasets, the model will be able to predict the authenticity of new articles. The system will be beneficial for media organizations, researchers, and the general public to reduce the impact of misinformation.

Admin Panel Features

• The admin shall be able to manage user accounts (add, update, delete).
• The admin shall upload and update training datasets for the AI model.
• The admin shall retrain the model when new data is added.
• The admin shall view overall system performance metrics (accuracy, precision, recall, F1-score).
• The admin shall generate system usage reports.
• The admin shall manage and delete the history of analyzed articles if required.
• The admin shall ensure data security and user privacy.

User Panel Features

• The user shall register and log in to the system.
• The user shall input news text or headlines for analysis.
• The user shall get classification results as "Real" or "Fake" with a confidence score.
• The user shall be able to view the history of previously analyzed articles.
• The user shall test the system with sample datasets provided.
• The user shall view graphical representations of results (charts, graphs).
• The user shall export classification results.