An individual-group-merchant relation model for identifying fake online reviews: an empirical study on a Chinese e-commerce platform

Yu, Chuanming; Zuo, Yuheng; Feng, Bolin; An, Liwei; Chen, Baiyun

doi:10.1007/s10799-018-0288-1

Cited by 27 publications

(15 citation statements)

References 24 publications

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“…Merchants do not effectively share data with their FRA team, but those mastering more information can make predominant decisions. Social media is widely used in the manual audit, and it is also an area with great potential [ 29 ]. Table 3 signifies the features and manifestations of e-commerce fraud.…”

Section: Methodsmentioning

confidence: 99%

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining

2022

Computational Intelligence and Neuroscience

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This study aims to identify e-commerce fraud, solve the financial risks of e-commerce enterprises through big data mining (BDM), further explore more effective solutions through Information fusion technology (IFT), and create an e-commerce fraud detection model (FDM) based on IFT (namely, computer technology (CT), artificial intelligence (AI), and data mining (DM). Meanwhile, BDM technology, support vector machine (SVM), logistic regression model (LRM), and the proposed IFT-based FDM are comparatively employed to study e-commerce fraud risks deeply. Specifically, the LRM can effectively solve data classification problems. The proposed IFT-based FDM fuses different information sources. The experimental findings corroborate that the proposed Business-to-Business (B2B) e-commerce enterprises-oriented IFT-based FDM presents significantly higher fraud identification accuracy than SVM and LRM. Therefore, the IFT-based FDM is superior to SVM and LRM; it can process and calculate e-commerce enterprises' financial risk data from different sources and obtain higher accuracy. BDM technology provides an important research method for e-commerce fraud identification. The proposed e-commerce enterprise-oriented FDM based on IFT can correctly analyze enterprises' financial status and credit status, obtaining the probability of fraudulent behaviors. The results are of great significance to B2B e-commerce fraud identification and provide good technical support for promoting the healthy development of e-commerce.

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Section: Methodsmentioning

confidence: 99%

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining

2022

Computational Intelligence and Neuroscience

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“…This work highlights the importance of user and product information for learning review representation. Yu et al ( 2019 ) mainly analyze the behavior of the stakeholders of the reviews and judge the falsity of the reviews. Specifically, they propose three indicators to calculate the fake degree of individuals, groups, and merchants.…”

Section: The Study Of Fake Reviewsmentioning

confidence: 99%

Graph Learning for Fake Review Detection

Ren

et al. 2022

Front. Artif. Intell.

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Fake reviews have become prevalent on various social networks such as e-commerce and social media platforms. As fake reviews cause a heavily negative influence on the public, timely detection and response are of great significance. To this end, effective fake review detection has become an emerging research area that attracts increasing attention from various disciplines like network science, computational social science, and data science. An important line of research in fake review detection is to utilize graph learning methods, which incorporate both the attribute features of reviews and their relationships into the detection process. To further compare these graph learning methods in this paper, we conduct a detailed survey on fake review detection. The survey presents a comprehensive taxonomy and covers advancements in three high-level categories, including fake review detection, fake reviewer detection, and fake review analysis. Different kinds of fake reviews and their corresponding examples are also summarized. Furthermore, we discuss the graph learning methods, including supervised and unsupervised learning approaches for fake review detection. Specifically, we outline the unsupervised learning approach that includes generation-based and contrast-based methods, respectively. In view of the existing problems in the current methods and data, we further discuss some challenges and open issues in this field, including the imperfect data, explainability, model efficiency, and lightweight models.

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“…The improvement of classification efficiency is only achieved by increasing the prediction accuracy of the algorithm. With the expanding application of SVM and its continuous research, researchers have found that the cost of misclassification of different samples varies greatly in contexts such as disease diagnosis and credit evaluation [31,33], which makes it impossible to identify more meaningful samples during prediction. Therefore, the cost of misclassification is regarded as an important factor that cannot be ignored.…”

Section: The Factors Of User Knowledge Attributesmentioning

confidence: 99%

“…Then, when inputting the corresponding data of new user identification factors, the lead users who meet the corresponding characteristic factors in other user groups or new user groups can be identified quickly [23,30]. Among intelligent machine learning methods, the support vector machine (SVM) method is widely used for its excellent binary classification performance [31]. The standard support vector machine method has good classification performance on class-balanced sample sets, while its classification performance on class-unbalanced sample sets is relatively poor.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

Chen

Zhang

et al. 2021

JTAER

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For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI.

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An individual-group-merchant relation model for identifying fake online reviews: an empirical study on a Chinese e-commerce platform

Cited by 27 publications

References 24 publications

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining

Graph Learning for Fake Review Detection

An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

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