Results 261 to 270 of about 346,740 (322)

Comparative Study of Machine Learning Algorithms for Fake Review Detection with Emphasis on SVM

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 2023
Online reviews have become an essential factor in consumer decision-making, with the credibility and authenticity of such reviews being a major concern.
Mr. P. Naresh, Samavedam Venkataramana, Naga Pavan, Abdul Razzakh Mohammed, N. Chanti, Modepu Tharun   +5 more
semanticscholar   +1 more source

Linguistic Features for Detecting Fake Reviews

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews.
Faranak Abri, Luis Felipe Gutierrez, Akbar Siami Namin, Keith S. Jones, David R. W. Sears   +4 more
openaire   +1 more source

Fake Review Detection Using Deep Neural Networks with Multimodal Feature Fusion Method

International Conference on Parallel and Distributed Systems, 2023
In order to enhance brand benefits or discredit competitors, some merchants hire fake reviewers to post large amounts of fake reviews on e-commerce platforms.
Xin Li, Lirong Chen
semanticscholar   +1 more source

Fake Review Detection Using Rating-Sentiment Inconsistency

International Conference on Machine Learning and Applications, 2023
Fake reviews have been a major problem in online platforms with detrimental effects on customer trust. Different machine learning and natural language processing methods have been used recently to classify fake reviews from authentic ones.
Kiana Sharifpour, Salim Lahmiri
semanticscholar   +1 more source

Robust Fake Review Detection Using Uncertainty-Aware LSTM and BERT

International Conference on Computational Intelligence and Communication Networks, 2023
In a web-based world driven by e-commerce, customers are quick to turn to online shopping services. However, the products available for purchase cannot be personally inspected so buyers turn to online product reviews.
Sarah Zabeen, Alina Hasan, Md. Farhadul Islam, Md Sabbir Hossain, Annajiat Alim Rasel   +4 more
semanticscholar   +1 more source

A Hybridized Approach for Enhanced Fake Review Detection

IEEE Transactions on Computational Social Systems
User reviews on online consumption platforms are crucial for both consumers and merchants, serving as a reference for purchase decisions and product improvement.
Shu Xu, Haoqi Cuan, Zhichao Yin, Chunyong Yin   +3 more
semanticscholar   +1 more source

MAGAT-HOS: A Multi-Attention Graph Neural Network for Fake Review Detection by Incorporating High-Order Semantic Information

IEEE International Joint Conference on Neural Network
The proliferation of fake reviews on e-commerce platforms has seriously and negatively affected consumers’ purchase decisions. In recent years, some researchers have started applying graph neural networks for fake review detection and achieved better ...
Yuanshuai Yao, Lirong Chen, Dongsong Zhang, Liuyang Qin   +3 more
semanticscholar   +1 more source

Leveraging Extravagant Linguistic Patterns to Enhance Fake Review Detection: A Comparative Study on Clustering Methods

2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE)
Online reviews are crucial in shaping consumer behavior and business success, but the growing presence of fake reviews threatens the credibility of these platforms. This paper introduces a novel approach to fake review detection by focusing on the use of
Mohammed Ennaouri, Imane Ettahiri, A. Zellou, Karim Doumi   +3 more
semanticscholar   +1 more source

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

Knowledge engineering review (Print)
Online reviews have a significant impact on the purchasing decisions of potential consumers. Positive reviews often sway buyers, even when faced with higher prices.
Richa Gupta, Vinita Jindal, Indu Kashyap
semanticscholar   +1 more source

Fake reviews detection based on LDA

2018 4th International Conference on Information Management (ICIM), 2018
It is necessary for potential consume to make decision based on online reviews. However, its usefulness brings forth a curse - deceptive opinion spam. The deceptive opinion spam mislead potential customers and organizations reshaping their businesses and prevent opinion-mining techniques from reaching accurate conclusions.
Shaohua Jia, Xianguo Zhang, Xinyue Wang, Yang Liu   +3 more
openaire   +1 more source