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In this research paper, we present a framework developed to differentiate between genuine and fake profiles on social networks. Utilizing a range of classification techniques—including random forests, support vector machines, and neural networks—we effectively categorize accounts as either real or fraudulent. This framework is especially beneficial for online social networks managing millions of profiles, as it automates the detection process, removing the need for manual review. Ultimately, this innovation has the potential to greatly enhance the security and reliability of online social communities.

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Identifying and Mitigating Fake Profiles on Social Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Arun Kumar Singh,
  • Sandeep Saxena,
  • Arjun Singh,
  • Payal Chhabra,
  • Prakash Kumar

摘要

In this research paper, we present a framework developed to differentiate between genuine and fake profiles on social networks. Utilizing a range of classification techniques—including random forests, support vector machines, and neural networks—we effectively categorize accounts as either real or fraudulent. This framework is especially beneficial for online social networks managing millions of profiles, as it automates the detection process, removing the need for manual review. Ultimately, this innovation has the potential to greatly enhance the security and reliability of online social communities.