The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review

Journal article


Authors/Editors


Research Areas


Publication Details

Author list: Reyes-Menendez A, Saura JR, Filipe F
Publisher: PeerJ
Place: LONDON
Publication year: 2019
Volume number: 5
Issue number: e219
ISSN: 2376-5992
Languages: English-Great Britain (EN-GB)


Abstract

In the last several decades, electronic word of mouth (eWOM) has been widely used by consumers on different digital platforms to gather feedback about products and services from previous customer behavior. However, this useful information is getting blurred by fake reviews-i.e., reviews that were created artificially and are thus not representative of real customer opinions. The present study aims to thoroughly investigate the phenomenon of fake online reviews in the tourism sector on social networking and online reviews sites. To this end, we conducted a systematic review of the literature on fake reviews for tourism businesses. Our focus was on previous studies that addressed the following two main topics: (i) tourism (ii) fake reviews. Scientific databases were used to collect relevant literature. The search terms "tourism" and "fake reviews" were applied. The database of Web of Science produced a total of 124 articles and, after the application of different filters following the PRISMA 2009 Flow diagram, the process resulted in the selection of 17 studies. Our results demonstrate that (i) the analysis of fake reviews is interdisciplinary, ranging from Computer Science to Business and Management, (ii) the methods are based on algorithms and sentiment analysis, while other methodologies are rarely used; and (iii) the current and future state of fraudulent detection is based on emotional approaches, semantic analysis and new technologies such as Blockchain. This study also provides helpful strategies to counteract the ubiquity of fake reviews for tourism businesses.


Keywords

No matching items found.


Documents

No matching items found.

Last updated on 2019-16-10 at 16:55