Fake Profile Detection Using Machine Learning

Authors

  • K. Harish  Department of Information Technology, Kings Engineering College, Sriperumbudur, Tamilnadu, India
  • R. Naveen Kumar  Department of Information Technology, Kings Engineering College, Sriperumbudur, Tamilnadu, India
  • Dr. J. Briso Becky Bell  Department of Information Technology, Kings Engineering College, Sriperumbudur, Tamilnadu, India

DOI:

https://doi.org/10.32628/IJSRSET2310264

Keywords:

Social Media - World - Rumours - Fake Profiles - Detection

Abstract

Platforms for social media like Facebook, Twitter, Instagram, and others have a big impact on our lives. All across the world, people are actively engaged in it. But, it also needs to address the problem of false profiles. Fake accounts are regularly made by people, software, or machines. They are employed in the spread of rumors and illegal actions like phishing and identity theft. This project uses several machine learning techniques to discriminate between fake and authentic Twitter profiles based on characteristics such as follower and friend counts, status changes, and more. Twitter profile dataset, classifying genuine accounts as TFP and E13 and fake accounts as INT, TWT, and FSF. In this section, the author talks about neural networks, LSTM, XG Boost, and Random Forest. The important traits are picked to judge the veracity of a social media page. The architecture and hyperparameters are also discussed. Lastly, after the models have been trained, results are generated. As a result, the output is 0 for true profiles and 1 for fake profiles. It is possible to disable or delete a fake profile when it is found, preventing cyber security issues.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

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How to Cite

[1]
K. Harish, R. Naveen Kumar, Dr. J. Briso Becky Bell "Fake Profile Detection Using Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.719-725, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRSET2310264