Fake review and reviewer detection through behavioral graph partitioning integrating deep neural network

Manaskasemsak, Bundit; Tantisuwankul, Jirateep; Rungsawang, Arnon

doi:10.1007/s00521-021-05948-1

Cited by 22 publications

(5 citation statements)

References 36 publications

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“…However, relationships between reviews, reviewers, and products may shed light on detecting spammers as well [43]. Such relationships have been taken into account by devising graph-based approaches [44], [45], [46]. Wang et al [3], [47] proposed the first graph-based approach to identify review spammers using store review data from www.resellerrating.com.…”

Section: Related Approachesmentioning

confidence: 99%

Understanding Large-Scale Network Effects in Detecting Review Spammers

Rout

Sahoo

Dalmia³

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Section: Related Approachesmentioning

confidence: 99%

Understanding Large-Scale Network Effects in Detecting Review Spammers

Rout

Sahoo

Dalmia³

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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“…Unsupervised methods apply clustering techniques (Liu & Pang, 2018) and graph-based analysis (Ye & Akoglu, 2015) for fake review detection without requiring labelled data. A graph partitioning approach (Manaskasemsak et al, 2023) is proposed to prevent the deceiving of untruthful reviews on product quality and fair commercial benefits. However, this approach focuses on distinguishing fake reviewers from benign ones rather than fake review detection.…”

Section: Fake Review Detectionmentioning

confidence: 99%

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

Chau

Nguyen

Tran³

et al. 2023

Australasian Marketing Journal

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With the advancement of internet technology, customers increasingly rely on online reviews as a valuable source of information. The study aims to develop a marketing data analytics framework to manage online reviews, especially fake reviews, which have become a significant issue undermining the creditability of online review systems. As small and medium-sized enterprises often lack the capabilities to automatically derive customer insights from online reviews, this study proposes a cost-effective, extensible Review-Analytics-as-a-Service (RAaaS) framework that can be operated by non-data specialists to facilitate online review data analytics. We demonstrate the framework’s application by using two datasets with more than 400,000 online reviews from Yelp to simulate live platforms and demonstrate an analytic flow of review fraud detection and understanding. The findings reveal insights into the influence of fake reviews on product ranking and exposure rate. Moreover, it was found that there was a higher concentration of sadness and anger in fake reviews (vs. organic reviews). In addition, fake reviews tend to be shorter, more extreme (with the use of strong adverbs), and have different patterns of topic distribution. This study has important implications for different stakeholder groups including, but not limited to, SMEs, review platforms and customers.

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“…Graph-based methods are used to capture relationships among products, stores, reviews and reviewers in a graph by representing each node of the graph with a set of features (Shehnepoor et al, 2017;Fang et al, 2020;Manaskasemsak et al, 2021). Machine learning methods can be further categorized into supervised, semi-supervised and unsupervised learning.…”

Section: Fake Review Detection In the Hospitality Industrymentioning

confidence: 99%