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With extensive access to the internet, information gets spread rapidly, leading to a generation of misinformation affecting the lives of people, businesses, etc. Researchers are eager to find a solution to the problem of generating error messages. Machine learning and deep learning-based approaches are becoming popular to develop auto-mated models to identify information calumny. The machine learning-based models are based on a feature extraction process. Handcrafted features play a crucial role in model development. This includes content or text and user-specific features. One type of significant feature is sarcasm, which is used to communicate emotions in a way opposite to what it actually means to be. This feature can undoubtedly assist in identifying the credibility of contents and information calumny. Previous researchers have studied the sarcasm in fake news but have not used these as features to develop models. They proposed a novel approach to identify information calumny with sarcasm to fill this gap and showed the highest accuracy, reaching 93.88%.

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A Novel Approach for Identification of Information Defamation Using Sarcasm Features

  • 会议论文

  • 发表日期：

    2026年

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  • Reshma Kohad,
  • Nidhi Khare,
  • Sachin Kadam,
  • Nidhi,
  • Vishal Borate,
  • Yogesh Mali

摘要

With extensive access to the internet, information gets spread rapidly, leading to a generation of misinformation affecting the lives of people, businesses, etc. Researchers are eager to find a solution to the problem of generating error messages. Machine learning and deep learning-based approaches are becoming popular to develop auto-mated models to identify information calumny. The machine learning-based models are based on a feature extraction process. Handcrafted features play a crucial role in model development. This includes content or text and user-specific features. One type of significant feature is sarcasm, which is used to communicate emotions in a way opposite to what it actually means to be. This feature can undoubtedly assist in identifying the credibility of contents and information calumny. Previous researchers have studied the sarcasm in fake news but have not used these as features to develop models. They proposed a novel approach to identify information calumny with sarcasm to fill this gap and showed the highest accuracy, reaching 93.88%.