Project Overview:

The Fake News Detection & Source Credibility System is a web-based application that uses machine learning and natural language processing (NLP) to detect potentially fake or misleading news articles. It also evaluates the credibility of news sources based on past reliability, bias, and content quality.

The system is designed to help users differentiate between legitimate news and misinformation, especially on social media and news platforms. It analyzes news content, headline tone, source history, and more to generate a credibility score.

Key Features:

1.  ML-based fake news classifier (Real vs Fake)

2.  Source credibility scoring system

3.  Text analysis (bias, sentiment, keyword matching)

4.  Option to input URLs or paste full articles

5.  Fact-check database cross-referencing (optional)

6.  User dashboard with history of checks

7.  Admin panel to manage articles, labels, and credibility ratings

Technology Stack:

 Backend:

• Node.js + MongoDB, or

• Java (Spring Boot) + MySQL, or

• PHP + MySQL

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript (AJAX, Chart.js)

• Optional: Vue.js or React.js for a dynamic UI

 Machine Learning & NLP (Python):

• Libraries: Scikit-learn, NLTK, SpaCy, TensorFlow/Keras

• Models: Logistic Regression, SVM, Naive Bayes, or BERT

• Dataset: FakeNewsNet, LIAR dataset, or your custom dataset

How It Works:

1. Input Options:

   • User submits a news article, headline, or URL

   • The content is extracted and preprocessed (stopwords removed, tokenized, etc.)

2. Classification:

   • ML model predicts whether the article is Fake or Real

   • Displays confidence score (e.g., 82% likely fake)

3. Source Credibility Module:

   • Checks source against a database of news sites with credibility ratings

   • Factors considered: past misinformation, political bias, source transparency

4. Text Analytics:

   • Sentiment analysis (tone of article)

   • Keyword pattern matching (clickbait detection)

   • Bias level (using NLP techniques)

5. Output:

   • Final result: Fake or Real

   • Source trust score (e.g., CNN – 87/100, xyznews.com – 35/100)