@Article{cmc.2020.09835,
AUTHOR = {Ye Wang, Bixin Liu, Hongjia Wu, Shan Zhao, Zhiping Cai, Donghui Li, Cheang Chak Fong},
TITLE = {An Opinion Spam Detection Method Based on Multi-Filters  Convolutional Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {355--367},
URL = {http://www.techscience.com/cmc/v65n1/39570},
ISSN = {1546-2226},
ABSTRACT = {With the continuous development of e-commerce, consumers show increasing 
interest in posting comments on consumption experience and quality of commodities. 
Meanwhile, people make purchasing decisions relying on other comments much more 
than ever before. So the reliability of commodity comments has a significant impact on 
ensuring consumers’ equity and building a fair internet-trade-environment. However, 
some unscrupulous online-sellers write fake praiseful reviews for themselves and
malicious comments for their business counterparts to maximize their profits. Those
improper ways of self-profiting have severely ruined the entire online shopping industry. 
Aiming to detect and prevent these deceptive comments effectively, we construct a model 
of Multi-Filters Convolutional Neural Network (MFCNN) for opinion spam detection. 
MFCNN is designed with a fixed-length sequence input and an improved activation
function to avoid the gradient vanishing problem in spam opinion detection. Moreover,
convolution filters with different widths are used in MFCNN to represent the sentences
and documents. Our experimental results show that MFCNN outperforms current stateof-the-art methods on standard spam detection benchmarks.},
DOI = {10.32604/cmc.2020.09835}
}