Camouflage is NOT easy: Uncovering adversarial fraudsters in large online app review platform

Wang, Yunlong; Wu, Chunming

doi:10.1177/0020294020970213

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While fake reviews have a significant impact on ecommerce, detecting them is crucial, but complicated. Detection of fake reviews is easy when the user shows apparent suspicious behavior, such as leaving reviews every day using different devices, because normal users do not post reviews daily and do not use various devices to do so [21]. However, this problem has become complicated because of the deception strategies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fake Online Reviews: A Unified Detection Model Using Deception Theories

2022

View full text Add to dashboard Cite

Online reviews influence consumers' purchasing decisions. However, identifying fake reviews online automatically remains a complex problem, and current detection approaches are inefficient in preventing the spread of fake reviews. The literature on fake reviews detection lacks a comprehensive and interpretable theory-based model with high performance, which enables us to understand the phenomenon from a psychological perspective and analyze reviews based on user-generated content as well as consumer behavior. In this research, we synthesized ten well-founded deception theories from psychology, namely leakage theory, four-factor theory, interpersonal deception theory, self-presentational theory, reality monitoring theory, criteria-based content analysis, scientific content analysis, verifiability approach, truthdefault theory, and information manipulation theory, and selected nine relevant constructs to develop a unified model for detecting fake online reviews. These constructs include specificity, quantity, nonimmediacy, affect, uncertainty, informality, consistency, source credibility, and deviation in behavior. We characterized the selected constructs using verbal and non-verbal features to validate the proposed model empirically. Subsequently, we extracted features from the Yelp datasets and used them to train four machine learning algorithms, specifically Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. We demonstrated that quantity, non-immediacy, affect, informality, consistency, source credibility, and deviation in behavior are essential constructs for detecting fake reviews. To our surprise, we discovered that non-verbal features are more important than verbal features and that combining features from both types improves the prediction performance. Our theory-based model outperformed most of the state-of-the-art fake review detection models and yielded high interpretability and low complexity.

show abstract

Section: Introductionmentioning

confidence: 99%

“…However, this problem has become complicated because of the deception strategies. Fraudulent users change their techniques to avoid detection systems [21]. Some of them attempt to appear normal by including links to well-known entities [11].…”

Section: Introductionmentioning

confidence: 99%

Fake Online Reviews: A Unified Detection Model Using Deception Theories

2022

View full text Add to dashboard Cite

show abstract

Recent state-of-the-art of fake review detection: a comprehensive review

Gupta,

Jindal,

Kashyap

2024

The Knowledge Engineering Review

View full text Add to dashboard Cite

Online reviews have a significant impact on the purchasing decisions of potential consumers. Positive reviews often sway buyers, even when faced with higher prices. This phenomenon has given rise to a deceptive industry dedicated to crafting counterfeit reviews. Companies frequently indulge in procuring bulk fake reviews, employing them to tarnish their rivals’ reputations or artificially bolster their credibility. These spurious reviews materialize through automated systems or compensated individuals. Thus, detecting fake reviews is becoming increasingly important due to their deceptive nature, as they are extremely difficult for humans to identify. To address this issue, current work has focused on machine learning and deep learning techniques to identify fake reviews. However, they have several limitations, including a lack of sufficient training data, inconsistency in providing accurate solutions across different datasets, concept drift, and inability to address new methods that evolved to create fake reviews over time. The objective of this review paper is to find the gaps in the existing research in the field of fake review detection and provide future directions. This paper provides the latest, comprehensive overview and analysis of research efforts focusing on various techniques employed so far, distinguishing characteristics utilized, and the existing datasets used.

show abstract

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Mutemi,

Bacao

2024

Big Data Min. Anal.

View full text Add to dashboard Cite

The e-commerce industry's rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.

show abstract

Camouflage is NOT easy: Uncovering adversarial fraudsters in large online app review platform

Cited by 3 publications

References 10 publications

Fake Online Reviews: A Unified Detection Model Using Deception Theories

Fake Online Reviews: A Unified Detection Model Using Deception Theories

Recent state-of-the-art of fake review detection: a comprehensive review

E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Contact Info

Product

Resources

About