Social Fraud Detection Review: Methods, Challenges and Analysis

TL;DR

This paper provides a comprehensive review of social fraud detection methods, analyzing challenges, approaches, and future directions across behavioral and text-based features, with a focus on supervised, semi-supervised, and unsupervised learning techniques.

Contribution

It offers an exhaustive literature review framework considering review, user, and item components, and categorizes detection approaches including classical and deep learning methods.

Findings

Supervised methods are effective but limited by lack of labeled data.

Deep learning approaches are increasingly used for fraud detection.

Future research should focus on developing labeled datasets and hybrid models.

Abstract

Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.