Scholarlink

In today’s digital age, the majority of smartphone users prefer reading news on social media rather than directly on news websites, which typically offer verified sources. This shift presents a significant challenge: verifying the authenticity of news and articles shared on plat- forms like Facebook Pages, Twitter, WhatsApp groups, and other microblogging and social networking sites. Believing and disseminating rumors as news can be detrimental to society, making it crucial to halt the spread of misinformation, especially in emerging nations like India. This study introduces a technique and model for the identification of false news. Our approach involves compiling news articles and applying various machine learning and deep learning models, including Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Long Short-Term Memory (LSTM) networks, to determine the authenticity of the news. By comparing the outputs of these models with existing models, we demonstrate that our proposed approach operates effectively, achieving an accuracy rate of up to 99%. This high level of accuracy underscores the potential of our model to contribute significantly to the fight against the spread of false information.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Expert System for Detecting Fake Content Using Machine Learning and Deep Learning Model Through Existing Dataset

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Makhan Kumbhkar,
  • Shraddha Masih,
  • Seema Kamble

摘要

In today’s digital age, the majority of smartphone users prefer reading news on social media rather than directly on news websites, which typically offer verified sources. This shift presents a significant challenge: verifying the authenticity of news and articles shared on plat- forms like Facebook Pages, Twitter, WhatsApp groups, and other microblogging and social networking sites. Believing and disseminating rumors as news can be detrimental to society, making it crucial to halt the spread of misinformation, especially in emerging nations like India. This study introduces a technique and model for the identification of false news. Our approach involves compiling news articles and applying various machine learning and deep learning models, including Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Long Short-Term Memory (LSTM) networks, to determine the authenticity of the news. By comparing the outputs of these models with existing models, we demonstrate that our proposed approach operates effectively, achieving an accuracy rate of up to 99%. This high level of accuracy underscores the potential of our model to contribute significantly to the fight against the spread of false information.