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Journals / Politeknik Dergisi / 2026 / Cilt: 27 Sayı: 2

Classification of Fake, Bot, and Real Accounts on Instagram Using Machine Learning

Makine Öğrenmesi ile Instagram'da Sahte, Bot ve Gerçek Hesapların Sınıflandırılması

Ümmü Tunç(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0000-0002-9022-9698
Esra Atalar(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0000-0002-1856-9487
Musa Sezer Gargı(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0000-0001-7817-3997
Zeliha Ergül Aydın(Eskişehir Teknik Üniversitesi)0000-0002-7108-8930
Pages
479–488
Publication Date
2024-03-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our results show that the Random Forest gives the highest prediction accuracy with 90.2%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Instagram is a social media platform that allows users to share content such as photos and videos.
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Key Findings & Data:Fake and bot account problems constitute a significant obstacle to social networking.
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Conclusion & Impact:Our results show that the Random Forest gives the highest prediction accuracy with 90.2%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sosyal medya · Instagram · sahte hesap tespiti

Abstract

Instagram is a social media platform that allows users to share content such as photos and videos. Fake and bot account problems constitute a significant obstacle to social networking. Since fake and bot accounts have purposes such as increasing the number of followers, creating a perception by using misinformation, deceiving people, detecting these fake and bot accounts plays an essential role in creating a secure social network. Fake account detection is beneficial to keeping people safe from misinformation and malicious profiles on Instagram, ensuring customers' safe accounts, and preventing fraud. From this point, we aim to classify Instagram user profiles into fake, bot, and real accounts with classification algorithms. Additionally, we present a publicly available dataset for the fake, bot, and real accounts detection on Instagram. For data collection, real accounts were determined from our circle of friends, fake accounts were accessed by manual scanning from Instagram, and bot accounts were accessed by purchasing from bot account websites and mobile applications. These accounts' features were collected via web scraping. We use the seven classifiers to train classification models in fake, bot, and real profile detection. Our results show that the Random Forest gives the highest prediction accuracy with 90.2%.

Özet

Instagram, kullanıcıların fotoğraf ve video gibi içerikleri paylaşmalarını sağlayan bir sosyal medya platformudur. Sahte ve bot hesap sorunları sosyal ağların önünde önemli bir engel oluşturmaktadır. Sahte ve bot hesapların takipçi sayısını artırmak, yanlış bilgiler kullanarak algı oluşturmak, insanları aldatmak, bu sahte ve bot hesapları tespit etmek gibi amaçları olduğundan güvenli bir sosyal ağ oluşturmada önemli rol oynar. Sahte hesap tespiti, insanları Instagram'daki yanlış bilgilerden ve kötü niyetli profillerden korumak, müşterilerin hesaplarının güvenliğini sağlamak ve dolandırıcılığı önlemek için faydalıdır. Bu noktadan hareketle, bu çalışma ile Instagram kullanıcı profillerini sınıflandırma algoritmaları ile fake, bot ve gerçek hesaplar olarak sınıflandırmayı amaçlanmaktadır. Ek olarak, Instagram'da sahte, bot ve gerçek hesap tespiti için herkese açık bir veri seti sunulmaktadır. Veri toplama aşamasında, gerçek hesaplar arkadaş çevremizden, sahte hesaplar Instagram paylaşımları manuel taranarak belirlenirken, bot hesaplara bot hesap siteleri ve mobil uygulamalardan satın alma işlemi ile ulaşılmıştır. Bu hesaplara ait öznitelikler ise web kazıma yoluyla toplanmıştır. Sahte, bot ve gerçek profil algılamada sınıflandırma modellerini eğitmek için yedi adet sınıflandırıcı kullanılmıştır. Sonuçlar, Rasgele Orman Sınıflandırıcısının %90,2 ile en yüksek tahmin doğruluğunu verdiğini göstermiştir.

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Cite This Article

Tunç, Ü., Atalar, E., Gargı, M. S., Aydın, Z. E. (2024). Classification of Fake, Bot, and Real Accounts on Instagram Using Machine Learning. Politeknik Dergisi, Cilt: 27 Sayı: 2, 479–488. https://doi.org/10.2339/politeknik.1136226

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