Social Fraud Detection Review: Methods, Challenges and Analysis
- URL: http://arxiv.org/abs/2111.05645v1
- Date: Wed, 10 Nov 2021 11:25:20 GMT
- Title: Social Fraud Detection Review: Methods, Challenges and Analysis
- Authors: Saeedreza Shehnepoor, Roberto Togneri, Wei Liu, Mohammed Bennamoun
- Abstract summary: Social reviews have dominated the web and become a plausible source of product information.
Business 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.
- Score: 42.30892608083864
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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