Manuscript Number : IJSRSET2310264
Fake Profile Detection Using Machine Learning
Authors(3) :-K. Harish, R. Naveen Kumar, Dr. J. Briso Becky Bell
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.
K. Harish
Social Media - World - Rumours - Fake Profiles - Detection
Publication Details
Published in :
Volume 10 | Issue 2 | March-April 2023 Article Preview
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
Date of Publication :
2023-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
719-725
Manuscript Number :
IJSRSET2310264
Publisher : Technoscience Academy
Journal URL :
https://res.ijsrset.com/IJSRSET2310264