A Comparative Study of Machine Learning Methods for Detection of Fake Online Consumer Reviews
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39916685" target="_blank" >RIV/00216275:25410/19:39916685 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/abs/10.1145/3383902.3383909" target="_blank" >https://dl.acm.org/doi/abs/10.1145/3383902.3383909</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3383902.3383909" target="_blank" >10.1145/3383902.3383909</a>
Alternative languages
Result language
angličtina
Original language name
A Comparative Study of Machine Learning Methods for Detection of Fake Online Consumer Reviews
Original language description
Online product reviews provide valuable information for consumer decision making. Customers increasingly rely on the reviews and consider them a trusted source of information. For businesses, it is therefore tempting to purchase fake reviews because competitive advantage can be easily achieved by producing positive or negative fake reviews. Machine learning methods have become a critical tool to automatically identify fake reviews. Recently, deep neural networks have shown promising detection accuracy. However, there have been no studies which compare the performance of state-of-the-art deep learning approaches with traditional machine learning methods, such as Naïve Bayes, support vector machines or decision trees. The aim of this study is to examine the performance of several machine learning methods used for the detection of positive and negative fake consumer reviews. Here we show that deep neural networks, including convolutional neural networks and long short term memory, significantly outperform the traditional machine learning methods in terms of accuracy while preserving desirable time performance.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICEBI 2019 : proceedings of the 2019 3rd International Conference on E-Business and Internet
ISBN
978-1-4503-7170-4
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
18-22
Publisher name
ACM (Association for Computing Machinery)
Place of publication
New York
Event location
Praha
Event date
Nov 9, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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