THE OVERVIEW OF Fake News Detection System (Real-Time Checker)
This project proposes a real-time web application that analyzes and checks news articles
and hesdlines to predict validity. In this age of information, false news and info travel fast
so with the use natural language processing and machine learning, the system helps users
verify the authenticity of online content before sharing.
THE OBJECTIVES OF Fake News Detection System (Real-Time Checker)
• Build a system that gives information on whether news is real or fake
• It’ll Provide a measure of credibility like a score for authenticity
• Maintain a database of flagged articles.
• Help users and journalists limit spread of fake and unverified news
INTRODUCTION
The spread of fake news has become a problem especially on social media platforms where
fake news are used as a form of income from clicks and likes etc. Fake news can influence a
whole host of things in one’s life namely politics, health and the reputation of public figures.
Automated fact-checking using AI can provide an accessible tool for users to verify content
credibility in real time.
System Architecture
Components:
- Data Layer: labeled articles, metadata, and fact-check sources.
- Processing Layer: text preprocessing, ML classification models.
- Application Layer: user input forms, results display, admin tools.
- Cloud Layer: storage, logging, and continuous learning pipelines.
Project Scope & Methodology
Scope:
The scope includes detecting fake news in text or links. The system will provide results
based on the links and titles and allow administrators to flag and report fake articles
Methodology:
• Gather datasets of verified and fake news.
• Train ML classifiers (transformers or logistic regression).
• Develop scraping and text cleaning pipeline.
• Deploy model as REST API and integrate with frontend.
Implementation Plan
Phase 1: Dataset preparation and preprocessing (4 weeks).
Phase 2: Model selection and training (5 weeks).
Phase 3: Web application development and API integration (5 weeks).
Phase 4: User testing and evaluation (3 weeks).
Phase 5: Deployment and optimization (3 weeks).
Benefits
• Supports journalists and students in verifying information.
• Reduces the risk of misinformation in public spaces
• Enhances public trust in shared online content.
Challenges & Mitigation
Potential bias in datasets could lead to inaccurate predictions.
Mitigation: diversify training data and use ensemble models.
High server demand during peak usage.
Mitigation: scale backend with load balancers.
Users may distrust predictions.
Mitigation: display transparency by showing evidence sources and confidence scores.
Conclusion
The Fake News Detection System will represent an innovative way to counter
misinformation. By giving instant credibility checks, it empowers users to make informed
decisions and reduces the harmful effects of false information online.
