Abstract :

The Rapid Expansion Of Social Networking Platforms Has Led To Unprecedented Levels Of User Interaction, Content Creation, And Digital Influence. However, This Growth Has Also Enabled The Proliferation Of Spambots And Fake Followers, Which Undermine Platform Integrity, Distort Public Opinion, And Facilitate Malicious Activities Such As Misinformation Spread, Financial Fraud, And Identity Manipulation. Traditional Detection Mechanisms Struggle To Balance Accuracy, Scalability, And Transparency, Especially As Spambot Behavior Becomes Increasingly Sophisticated And Human-like. This Study Proposes An Interpretable AI-based Machine Learning Framework For The Identification Of Spambots And Fake Followers On Social Networks, Emphasizing The Use Of Logistic Regression For Explainability And Reliability. Unlike Black-box Deep Learning Models, Logistic Regression Enables Transparent Decision-making By Clearly Associating Feature Contributions With Classification Outcomes. The Research Explores Behavioral, Content-based, And Network-centric Features See As Posting Frequency, Follower-following Ratios, Temporal Activity Patterns, And Interaction Diversity. The Proposed System Aims To Achieve High Detection Accuracy While Maintaining Interpretability, Which Is Critical For Trust, Regulatory Compliance, And Platform Governance. Additionally, The Abstract Discusses How Quantum-inspired Advantages Such As Parallel Feature Evaluation And Optimization Could Further Enhance Scalability And Performance In Large-scale Social Network Environments. The Results Of This Approach Demonstrate That Interpretable AI Can Effectively Counter Spam-driven Manipulation While Providing Actionable Insights For Administrators And Policymakers.The Rapid Expansion Of Social Networking Platforms Has Led To Unprecedented Levels Of User Interaction, Content Creation, And Digital Influence. However, This Growth Has Also Enabled The Proliferation Of Spambots And Fake Followers, Which Undermine Platform Integrity, Distort Public Opinion, And Facilitate Malicious Activities Such As Misinformation Spread, Financial Fraud, And Identity Manipulation. Traditional Detection Mechanisms Struggle To Balance Accuracy, Scalability, And Transparency, Especially As Spambot Behavior Becomes Increasingly Sophisticated And Human-like. This Study Proposes An Interpretable AI-based Machine Learning Framework For The Identification Of Spambots And Fake Followers On Social Networks, Emphasizing The Use Of Logistic Regression For Explainability And Reliability. Unlike Black-box Deep Learning Models, Logistic Regression Enables Transparent Decision-making By Clearly Associating Feature Contributions With Classification Outcomes. The Research Explores Behavioral, Content-based, And Network-centric Features See As Posting Frequency, Follower-following Ratios, Temporal Activity Patterns, And Interaction Diversity. The Proposed System Aims To Achieve High Detection Accuracy While Maintaining Interpretability, Which Is Critical For Trust, Regulatory Compliance, And Platform Governance. Additionally, The Abstract Discusses How Quantum-inspired Advantages Such As Parallel Feature Evaluation And Optimization Could Further Enhance Scalability And Performance In Large-scale Social Network Environments. The Results Of This Approach Demonstrate That Interpretable AI Can Effectively Counter Spam-driven Manipulation While Providing Actionable Insights For Administrators And Policymakers.

Published:

12-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

691-696

Section:

Articles

License:

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

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