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Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection

Author

Listed:
  • Najla Alharbi

    (Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia)

  • Bashayer Alkalifah

    (Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia)

  • Ghaida Alqarawi

    (Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia)

  • Murad A. Rassam

    (Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia)

Abstract

An online social media platform such as Instagram has become a popular communication channel that millions of people are using today. However, this media also becomes an avenue where fake accounts are used to inflate the number of followers on a targeted account. Fake accounts tend to alter the concepts of popularity and influence on the Instagram media platform and significantly impact the economy, politics, and society, which is considered cybercrime. This paper proposes a framework to classify fake and real accounts on Instagram based on a deep learning approach called the Long Short-Term Memory (LSTM) network. Experiments and comparisons with existing machine and deep learning frameworks demonstrate considerable improvement in the proposed framework. It achieved a detection accuracy of 97.42% and 94.21% on two publicly available Instagram datasets, with F-measure scores of 92.17% and 89.55%, respectively. Further experiments on the Twitter dataset reveal the effectiveness of the proposed framework by achieving an impressive accuracy rate of 99.42%.

Suggested Citation

  • Najla Alharbi & Bashayer Alkalifah & Ghaida Alqarawi & Murad A. Rassam, 2024. "Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection," Future Internet, MDPI, vol. 16(10), pages 1-22, October.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:10:p:367-:d:1496646
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    References listed on IDEAS

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    1. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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