A novel machine learning‐based framework for detecting fake Instagram profiles
Abstract
Summary: Recently, there has been a massive rise in the popularity of Instagram, which connects individuals globally and allows videos and images to be uploaded and exchanged, and communicated over social media. Instagram is also an online playground of deceit. The use of filters, lighting, and cunning angles transforms the mundane into something spectacular. Automated spam accounts and fake profiles use this to their malicious advantage for executing attacks targeting high‐profile executives. Creating fake Instagram identities is easy to reproduce the idea of being accepted by many fans on social media. Fake accounts are used in the marketing of fake services and products. This research focused on designing and training a unique neural network model and proposed a new algorithm for detecting automated spam and fake Instagram account profiles. The precision and accuracy of the proposed method were achieved at 93% and 91%, respectively.
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Fraudulent profiles create challenges for businesses that use social media for selling and appointment, as they may target fake or inactive accounts.
“Fraudulent profiles can create challenges for businesses that utilize social media for selling and appointment, as they may inadvertently target numerous fake or inactive accounts.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [18]
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References 17 · Cited by 37
- Authors
- Kaushik, K., Bhardwaj, A., Kumar, M., Gupta, S. K., & Gupta, A.
- Affiliations
- University of Petroleum and Energy Studies (IN)
- University of Wollongong in Dubai (AE)
- Shri Mata Vaishno Devi University (IN)
- Corresponding author
- Kumar, Manoj
- Gupta, Sachin Kumar
- Venue
- Concurrency and Computation: Practice and Experience
- Publisher
- Wiley
- Published
- 2022-10
- Volume
- 34
- Issue
- 28
- ISSN
- 1532-0626 (ISSN-L)
- 1532-0634
- OpenAlex
- W4298394334
- Cited by
- 37
- References
- 17
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 17 · 2023: 10
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Concurrency and Computation Practice and Experience (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- neural network classifier, online fraud
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