AI Fake News Detection | Final Year Project 2026
AI Fake News Detection faster than ever. From social media to online blogs, misinformation is influencing public opinion daily. As technology students, what if we could build a system that detects fake news AND calculates a credibility score?
In this blog, we will explore a powerful and practical AI Final Year Project called AI FactShield – Fake News & News Credibility Analyzer. This is not just a basic ML project. It is a complete web-based system built using Python, Flask, and NLP techniques.
Project tutorials, coding guides & placement tips for students.
AI Fake News Detection Project Overview
Project Name: AI FactShield – Fake News & News Credibility Analyzer
Technology Used: Python, Flask, Scikit-learn, SQLite, HTML, CSS, JavaScript
Type: Machine Learning + Web Application
Level: Final Year (BCA, B.Tech, MCA, MSc IT)
This project detects whether a news article is Real or Fake and also generates a credibility score (0–100%). It includes sentiment analysis, suspicious keyword highlighting, and an admin dashboard.
Problem Statement
Fake news spreads rapidly on digital platforms, causing misinformation and confusion. Most existing detection systems only classify news as fake or real, without providing deeper insights like bias, sentiment, or trust score.
This project aims to build an AI-powered web system that not only detects fake news but also calculates credibility score and stores analysis history.
Why AI Fake News Detection Project is NEW
- Not just Fake/Real classification
- Generates News Credibility Score (0–100%)
- Performs Sentiment Analysis
- Highlights suspicious words
- Stores user history in database
- Admin dashboard included
- REST API support (optional)
Most college projects stop at prediction. This project goes beyond and gives a real-world product feel.
Features
- Core Features
- Fake / Real prediction using Logistic Regression
- TF-IDF vectorization
- Credibility score calculation
- Sentiment polarity detection
- Suspicious keyword highlighting
- Admin login panel
- SQLite database integration
- Optional Advanced Features
- News URL scraping
- Source domain credibility database
- Downloadable PDF report
- Analytics dashboard
- Multi-language support
Machine Learning Approach
This project uses simple and explainable ML logic, which is easy to explain in viva:
- Text preprocessing (stopword removal, punctuation cleaning)
- TF-IDF feature extraction
- Logistic Regression model
- Sentiment analysis using TextBlob
The credibility score is calculated using model probability + sentiment polarity + suspicious keyword ratio.
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Real-World Use Case
- News readers verifying articles
- Journalists checking credibility
- Social media content moderators
- Educational institutions
- Bloggers and researchers
This can later be expanded into a Chrome Extension or SaaS product.
3 Important Viva Questions
Why did you choose Logistic Regression instead of Deep Learning?
Because Logistic Regression is simple, fast, and explainable. It performs well on text classification using TF-IDF features and is easy to justify in academic presentations.
How is credibility score calculated?
The credibility score is calculated using model prediction probability, sentiment polarity score, and suspicious keyword frequency.
How can this project be improved in the future?
It can be enhanced using deep learning models, real-time news API integration, multi-language support, and browser extensions.
Tech Stack
- Frontend: HTML, CSS, JavaScript
- Backend: Flask (Python)
- Database: SQLite
- Machine Learning: Scikit-learn
- NLP: TextBlob
Sample News URLs for Testing
Likely REAL News (from reputable sources)
| # | Source | URL |
|---|---|---|
| 1 | BBC News | https://www.bbc.com/news/world |
| 2 | Fake New Article | https://www.aljazeera.com/news/2026/2/20/hungary-to-block-90-billion-euro-eu-loan-to-ukraine-in-russian-oil-dispute |
How to Find Good Test Articles
- Go to any news website listed above
- Click on a specific article (not the homepage)
- Copy the full URL from the browser address bar
- Paste it into VerifyNews and click Analyze Article
Important Notes
- The model works best with English-language news articles
- Use full article URLs, not homepage or category links
- The model was trained on political news — it performs best on that genre
- Results are probabilistic; always cross-reference with other sources
- Some websites may block scraping, causing errors
Example Workflow
1. Open https://www.bbc.com/news
2. Click any article (e.g., a headline story)
3. Copy the URL like: https://www.bbc.com/news/articles/some-article-id
4. Paste into VerifyNews → Click "Analyze Article"
5. See REAL or FAKE resultWant Complete Source Code + Documentation?
Want Complete Source Code + Documentation?
If you are a final year student and want a ready-to-run project (with proper documentation, DB setup, and full dashboard), get the full project package from updategadh.com and save your time. Because last-minute project stress is real.
This project package includes:
- Complete Source Code
- Trained ML Model
- Dataset
- Page Documentation + PPT (Kindly inform us at least 1 days in advance ₹499 for that)
- Installation Guide
- System Architecture Diagram
Perfect for BCA, MCA, B.Tech, and MSc IT students.
If you are looking for a unique, practical, and sellable final year project in AI and Machine Learning, this Fake News & Credibility Analyzer is a strong choice. It is easy to explain, real-world applicable, and impressive in front of examiners.
Keywords
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