Overview:

The Fake News Detection System is an AI/ML-based web application designed to identify whether a given news article, post, or statement is real or fake.
With the rapid spread of misinformation on social media and digital platforms, this system helps users verify the authenticity of news content using Natural Language Processing (NLP) and Machine Learning algorithms.

The system classifies news as “Real” or “Fake” based on text analysis, linguistic patterns, and historical data.

Objectives:

• To combat the spread of fake news across digital platforms.

• To build an automated system that detects misinformation using ML models.

• To provide an easy-to-use interface for verifying news content.

Key Features:

1. News Input Module: Users can enter or paste news content or upload a news article.

2. ML-based Classification: The system uses trained models to classify news as Real or Fake.

3. Prediction Probability: Displays confidence score (e.g., 89% chance of being fake).

4. Dataset Integration: Uses pre-trained datasets (like Kaggle’s Fake News Dataset).

5. Admin Dashboard: Allows model retraining and dataset updates.

6. Keyword Extraction: Highlights suspicious words or biased language in the article.

7. Source Verification: Option to check the credibility of the news source.

8. Responsive Interface: Works smoothly on desktop and mobile browsers.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Node.js / PHP / Python (Flask or Django preferred for ML integration)

• Database: MySQL / MongoDB

• Machine Learning:

  • Algorithms: Logistic Regression, Naïve Bayes, or LSTM

  • Libraries: scikit-learn, pandas, NumPy, NLTK, TensorFlow

• APIs: NewsAPI or Google News API (for real-time data collection)

Workflow:

1. User enters the news text or uploads a news file.

2. The app processes the text through NLP (tokenization, stopword removal, lemmatization).

3. The trained ML model analyzes linguistic and semantic patterns.

4. The system predicts whether the news is Real or Fake with a confidence score.

5. Results are displayed to the user in a simple, color-coded format (e.g., green for real, red for fake).

Use Case Example:

A user receives a viral WhatsApp message claiming a government policy change. They paste the text into the Fake News Detection System, which processes the content and classifies it as “Fake (95% confidence)”, helping the user verify authenticity before sharing it further.

Future Enhancements:

• Integration with browser extensions for real-time fake news detection.

• Multilingual support to detect fake news in regional languages.

• AI-based source credibility scoring system.

• Blockchain integration for verifiable news authenticity records.