Advanced Algorithmic Approaches for Scam Profile Detection on Instagram
Open accessAbstract
Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model’s performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception.
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Social media networks' legitimacy may be threatened by fake accounts, damaging users' confidence and reputations.
“Social media networks’ legitimacy may be threatened by accounts, which would have a damaging effect on users’ confidence and reputations.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [12]
The Literature Review (section 2; page unavailable) describes fake profiles as a threat to user safety and says scams prey on user confidence. The Conclusions (section 6; page unavailable) link detecting fake profiles to a more trustworthy online community. These passages support the concern about confidence and trust, but do not address damage to users’ reputations. (LLM, conf=0.83)
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Cited by
Sorted by citations
Instagram fake profile detection using an ensemble learning method
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- Authors
- Bokolo, B. G., & Liu, Q.
- Affiliations
- Sam Houston State University (US)
- Corresponding author
- Bokolo, Biodoumoye George
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2024-04-19
- Volume
- 13
- Issue
- 8
- Pages
- 1571
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4394952484
- Cited by
- 6
- References
- 19
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- Instagram, online fraud, social media forensics, ensemble methods, random forest, XGBoost, gradient boosting, machine learning classification
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