Amdocs

Fact-Checker AI: Real-Time Misinformation Detection System

Overview

Fact-Checker AI is an advanced misinformation detection and fact-checking application designed to verify the credibility of online content. It leverages machine learning (ML), deep learning (DL), web scraping, and retrieval-augmented generation (RAG) to provide real-time validation of text-based information shared on social media and other digital platforms.

This system aims to reduce the spread of misinformation, enhance user trust in online content, and offer scalable, automated verification tools for individuals, businesses, and media organizations.

Key Features

• Automated Fact-Checking: Uses AI-driven models to analyze and validate text-based content.
• Real-Time Web Scraping: Gathers relevant data from trusted online sources to cross-check claims.
• LLM-Based Analysis: Employs large language models (LLMs) to understand context and assess credibility.
• Database Integration: Stores validated fact-checking results for future references and model improvement.
• User-Friendly API: Enables seamless integration with third-party applications for fact verification.

---

System Architecture

The system follows a structured flow, as illustrated in the provided architecture:

1. User Input:

   • The user submits a text/article for verification.
   • The system processes the request and determines the best validation approach.

2. LLM Model Processing:

   • The input is analyzed using an LLM (Large Language Model) for context understanding.
   • If additional verification is required, the system proceeds with data retrieval.

3. Web Scraper Module:

   • A web scraping tool fetches relevant information from trusted sources.
   • The extracted data is sent for processing.

4. Data Processing and Validation:

   • The retrieved data is structured and processed to determine credibility.
   • NLP and RAG techniques help cross-check the accuracy of claims.

5. Gen AI Model:

   • A Generative AI model evaluates the processed data to generate insights.
   • The model flags misinformation and provides fact-based responses.

6. Database Integration:

   • All verified information is stored in a database for learning and future verification.
   • The system continuously improves based on historical data.

7. User Output:

   • The verified response is presented to the user in a clear and concise format.
   • The user can see whether the input content is true, false, or requires further verification.

---

Technology Stack

• Machine Learning & AI:

  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • Retrieval-Augmented Generation (RAG)

• Data Processing:

  • Python, TensorFlow, PyTorch
  • Web Scraping (BeautifulSoup, Scrapy, Selenium)
  • APIs for trusted fact-checking sources

• Backend & Database:

  • FastAPI / Flask (API Development)
  • PostgreSQL / MongoDB (Database for storing validated content)

---

Usage & API Integration

1. Submit Content for Fact-Checking:

   • Users can input text/articles directly through the web interface or API.

2. Get Real-Time Verification:

   • The application analyzes the content and returns results with confidence scores.

3. Integrate with Third-Party Platforms:

   • The open API allows social media platforms, news agencies, and organizations to integrate the fact-checker into their systems.

---

Future Enhancements

• Multilingual Misinformation Detection
• Expansion to Image/Video-Based Fact-Checking
• Access to Non-Public APIs for Broader Verification
• Improved AI Training for Faster & More Accurate Analysis

---

Impact & Business Value

• Enhances online content credibility
• Reduces misinformation spread
• Increases trust in digital platforms
• Empowers users with real-time fact-checking capabilities

---

Contributors

Team Ensemble

• Ashika Singh
• Rahul Raj
• Saket Hatwar
• Sumit Singh

---