A Comparative Study of Machine Learning Methods for Detection of Fake Online Consumer Reviews

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparative Study of Machine Learning Methods for Detection of Fake Online Consumer Reviews

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Comparative Study of Machine Learning Methods for Detection of Fake Online Consumer Reviews

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICEBI 2019 : proceedings of the 2019 3rd International Conference on E-Business and Internet

ISBN

978-1-4503-7170-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

18-22

Název nakladatele

ACM (Association for Computing Machinery)

Místo vydání

New York

Místo konání akce

Praha

Datum konání akce

9. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—