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Automated Fake News Detection Using Machine Learning Techniques

  • Unique Paper ID: 168758
  • Volume: 11
  • Issue: 5
  • PageNo: 1817-1820
  • Abstract:
  • The proposed invention is an Automated Detection and Classification System that leverages machine learning techniques to identify and categorize fake news in real time. It processes large volumes of news content from sources such as social media, websites, and blogs using Natural Language Processing and supervised ML models trained on datasets containing labeled fake and legitimate news. The system first preprocesses data by cleaning and tokenizing news articles, followed by advanced feature extraction techniques like TF-IDF, word embeddings or BERT to capture the contextual nuances. It then applies classification models, such as Support Vector Machines, Random Forests, or deep learning approaches like LSTMs, to detect patterns that distinguish fake from real news. The system can also perform multimodal analysis by incorporating images, headlines, and metadata. To ensure transparency, it includes explainability methods like SHAP or LIME to provide insights into its classification decisions. This invention aims to reduce misinformation by offering a fast, scalable, and reliable solution for fake news detection across various platforms.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 1817-1820

Automated Fake News Detection Using Machine Learning Techniques

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