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Comprehensive Analysis of Online Social Network Frauds

Smita Bharne ; Pawan Bhaladhare (2023) — Advances in Data-Driven Computing and Intelligent Systems; Lecture Notes in Networks and Systems 698

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DOI: 10.1007/978-981-99-3250-4_3 (opens in a new tab)

Country:   Global
Tags:   Dating Fraud, False Identities, Fraud Detection, Machine Learning, Online Dating, Online Social Network Fraud, Romance Scams, Social Media
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Synopsis

            
This publication presents a comprehensive analysis of frauds in online social networks (OSNs), focusing on how frauds arise from the open, highly connected nature of OSNs and the diverse ways users interact on platforms such as Facebook, Instagram, and Twitter. It situates OSN fraud as a persistent cybercrime risk that targets adults, children, and women, and it surveys the landscape by outlining the purposes, harms, and observed trends behind various fraud types. The work combines a descriptive taxonomy with a synthesis of recent literature on detection, emphasizing machine learning (ML) as the leading data-driven approach while acknowledging the ongoing challenges posed by the complexity of OSNs. The authors categorize OSN frauds by their core intention, detailing six main classes: social engineering, human-targeted frauds, false identities, misinformation, and e-commerce/consumer frauds. Within these classes, they elaborate specific modalities such as social phishing, pretexting, baiting, cyberbullying, cyberstalking, cyber grooming, dating fraud, fake accounts, cloned and compromised accounts, and misinformation campaigns (fake news, rumors, and spam). A dedicated section describes e-commerce frauds (card testing, brand jacking, investment scams, and online shopping scams) and notes that investment and dating fraud, along with online shopping fraud, were prominent according to 2021 FTC reports. The interrelationship among OSN fraud, broader social-network threats, and cybercrime is framed to show overlapping risk structures. The chapter also surveys ML-based detection methods, summarizing various feature sets (profile, text content, and hybrid approaches) and reporting on a range of techniques used to identify fraudulent accounts, spammers, phishing sites, and related abuse. It concludes by comparing pros and cons of ML-driven detection, highlighting privacy concerns, data collection costs, and the ongoing need to improve detection, prevention, and control as OSNs evolve.

          

        
                  

            
Identified Gaps

            
Despite substantial research, online social network fraud remains unresolved and new fraud types are expected. Machine-learning detection faces challenges from the complex nature of online social networks, privacy constraints on obtaining behavioral data, costly data collection, unreliable self-reported profile information, and text features that may miss interaction patterns.

          

        
                  

            
Methods

            
This is a narrative, literature-based chapter. It classifies online social network fraud by fraud intent, summarizes reported global fraud statistics, describes dating fraud alongside other fraud categories, and reviews machine-learning detection studies. The review organizes detection methods by fraud type, algorithm, features, and dataset.

          

        
                  

            
Limitations

            
The chapter notes that collecting profile and behavioral data can violate privacy and may require owner consent. Such data collection can be costly or time-consuming under privacy protections. Malicious users may submit false profile information, while text-based features may not capture interaction patterns, comment frequency, or likes.

          

        
                  

            
Future Work

            
Detection, prevention, and control of online social network fraud should be continuously improved to address anticipated new forms of fraud. The chapter also identifies challenges in applying machine learning under privacy constraints and complex social-network environments.

          

              

    
    

        
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