Detecting Fake Accounts on Social Media Using Machine Learning

Year : 2024 | Volume : 11 | Issue : 02 | Page : 21 32
    By

    Abhishek Agrawal,

  • Priyanka Baste,

  • Snigdha Take,

  • Sachin Tupe,

  • S.N. Botekar,

  1. Student, Department of Computer Engineering, Jawahar Education Society’s Institute of Engineering, Nashik, Maharashtra, India
  2. Student, Department of Computer Engineering, Jawahar Education Society’s Institute of Engineering, Nashik, Maharashtra, India
  3. Student, Department of Computer Engineering, Jawahar Education Society’s Institute of Engineering, Nashik, Maharashtra, India
  4. Student, Department of Computer Engineering, Jawahar Education Society’s Institute of Engineering, Nashik, Maharashtra, India
  5. Professor, Department of Computer Engineering, Jawahar Education Society’s Institute of Engineering, Nashik, Maharashtra, India

Abstract

The growing frequency of fake accounts on social media platforms underscores the critical necessity for effective detection methods. In response to this challenge, our study leverages state-of-the-art machine learning techniques to identify and counter deceptive entities effectively. By conducting a thorough analysis of social media data, our approach unveils intricate patterns indicative of fraudulent accounts, enabling proactive measures against them. Through the application of advanced algorithms, we present a comprehensive methodology for the identification and mitigation of such accounts, addressing current online security challenges and anticipating future trends. Moreover, our research not only contributes significantly to enhancing cybersecurity within the realm of social media but also lays the groundwork for future advancements in this field. By integrating machine learning methods, our study offers a nuanced and highly effective approach, thereby bolstering the overall security landscape of online platforms. This paper serves to underscore the considerable strides made in social media security and provides a robust foundation for further research and development initiatives aimed at safeguarding users from malicious activities.

Keywords: Machine learning (ML), natural language processing (NLP), receiver operating characteristic (ROC), area under the curve (AUC), true positive rate (true positive rate), false positive rate (FPR), support vector machine (SVM).

[This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

How to cite this article:
Abhishek Agrawal, Priyanka Baste, Snigdha Take, Sachin Tupe, S.N. Botekar. Detecting Fake Accounts on Social Media Using Machine Learning. Journal of Mobile Computing, Communications & Mobile Networks. 2024; 11(02):21-32.
How to cite this URL:
Abhishek Agrawal, Priyanka Baste, Snigdha Take, Sachin Tupe, S.N. Botekar. Detecting Fake Accounts on Social Media Using Machine Learning. Journal of Mobile Computing, Communications & Mobile Networks. 2024; 11(02):21-32. Available from: https://journals.stmjournals.com/jomccmn/article=2024/view=152515


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 22/03/2024
Accepted 25/06/2024
Published 29/06/2024
1,049

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