Abstract :

The Proliferation Of Spam Analysis And False User Identities Has Made It More Challenging To Establish Trust And Safety On Social Media. The Exponential Development Of Social Media Has Facilitated The Adoption Of Numerous Illegal Behaviors, Including Misinformation, Trend Manipulation, And Identity Theft. Spam And False Profiles Have A Detrimental Effect On Both Online User Engagement And Social Media. Graphbased Techniques, Machine Learning, And Natural Language Processing Have Made It Simpler Than Ever To Detect Spam And Imposters. The Objective Of This Project Is To Create Algorithms That Can Detect False Profiles And Spam By Analyzing User Behavior, Content Quality, And Network Connections. The Paper Addresses Data Scarcity, Account Impersonation, And Content Manipulation. This Encompasses User Classification, Guided And Unsupervised Message Classification, And Real-time Monitoring Systems. Textual And Behavioral Analytics Have The Potential To Enhance The Precision Of User Detection And Social Network Security, As Evidenced By Our Observations. Index Terms: Fake User Detection, Spam Analysis, Social Networks, Machine Learning, Natural Language Processing (NLP), Account Impersonation, Content Manipulation

Published:

11-8-2025

Issue:

Vol. 25 No. 8 (2025)

Page Nos:

29-36

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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