Social Media Bot Fraud and Automated Abuse: Detection, Risk Scoring, and Real-Time Mitigation Frameworks

Abstract

The exponential growth of social media platforms has transformed global communication while simultaneously expanding the digital attack surface for malicious activities. Modern platforms that rely on user-generated content and engagement-driven algorithms have become increasingly vulnerable to bot-driven fraud, coordinated manipulation, and impersonation attacks. These threats undermine information integrity, distort public discourse, and expose users to phishing, spam, and large-scale abuse. This study presents a comprehensive detection framework that integrates multi-dimensional feature engineering with hybrid machine learning and graph-based analytics to address the evolving complexity of automated attacks. The proposed approach captures account-level attributes, behavioral patterns, content characteristics, and network interactions to distinguish between legitimate users and coordinated malicious entities. In addition, a dynamic behavioral risk scoring mechanism is introduced to evaluate the likelihood of automation and malicious intent in real time, enabling adaptive and prioritized response strategies. Temporal analysis is incorporated to identify synchronized activities and burst patterns commonly associated with coordinated bot campaigns.  Experimental evaluation demonstrates improved detection accuracy and enhanced visibility into coordinated attack structures compared to traditional approaches. The findings highlight the effectiveness of combining behavioral analytics with network intelligence in identifying sophisticated bot activities. The proposed framework offers a scalable and explainable solution for modern content moderation systems, supporting timely intervention while maintaining transparency and fairness in automated decision-making processes.