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Today’s world is a digital one, where news and information flow rapidly. However, fake news presents a major challenge, affecting public perception and decision-making. This paper introduces a machine learning (ML)-based real-time fake news detection system using web scraping, data preprocessing, feature engineering, and model building. The system incorporates generative AI models like DALL-E and CLIP for multi-modal content analysis. Our empirical results indicate that Logistic Regression achieves the highest accuracy of 99.976%. The proposed approach enhances misinformation detection while promoting media literacy.

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Real-Time Fake News Detection Using Machine Learning and Generative AI

  • 会议论文

  • 发表日期：

    2026年

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  • Shubhangi Jethwa,
  • Pamidi Firdous,
  • Abhishek Singh,
  • Trilok Nath Pandey,
  • Pankaj Shukla

摘要

Today’s world is a digital one, where news and information flow rapidly. However, fake news presents a major challenge, affecting public perception and decision-making. This paper introduces a machine learning (ML)-based real-time fake news detection system using web scraping, data preprocessing, feature engineering, and model building. The system incorporates generative AI models like DALL-E and CLIP for multi-modal content analysis. Our empirical results indicate that Logistic Regression achieves the highest accuracy of 99.976%. The proposed approach enhances misinformation detection while promoting media literacy.