Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Fake Social Media Profile Detection Using Machine Learning

Fake Social Media Profile Detection Using Machine Learning

Tiwari, Shlok
Reche, Nikhil
Raut, Bhavesh

Paperback

Technology & Engineering

ISBN10: 620843663X
ISBN13: 9786208436636
Publisher: LAP Lambert Academic Publishing
Published: May 13 2025
Pages: 52
Weight: 0.18
Height: 0.12 Width: 6.00 Depth: 9.00
Language: English
In today's connected world, social media has become a vital part of our daily lives. From sharing updates and connecting with friends to exchanging ideas and accessing news, these platforms offer immense value. But alongside their popularity, a concerning issue has grown the rise of fake profiles. These accounts can be used for a variety of harmful activities spamming, phishing, spreading misinformation, manipulating opinions, or even harassing others. With millions of users online, it's nearly impossible to identify these profiles manually. This is where intelligent, automated systems come into play. This project focuses on using XGBoost (Extreme Gradient Boosting) a fast and powerful machine learning algorithm that is used to detect fake social media profiles. XGBoost is well-known for handling structured data and performing better than many traditional models, thanks to its boosting technique and inbuilt regularization that helps prevent overfitting. To train the model, we used a dataset containing various features gathered from public user profiles.

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