The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review
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 cr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2019-09-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-219.pdf |
| _version_ | 1818256251808645120 |
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| author | Ana Reyes-Menendez Jose Ramon Saura Ferrão Filipe |
| author_facet | Ana Reyes-Menendez Jose Ramon Saura Ferrão Filipe |
| author_sort | Ana Reyes-Menendez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T17:24:47Z |
| format | Article |
| id | doaj.art-425aaaa279b44e5c8a58e0c0a16e6af0 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-12T17:24:47Z |
| publishDate | 2019-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-425aaaa279b44e5c8a58e0c0a16e6af02022-12-22T00:17:34ZengPeerJ Inc.PeerJ Computer Science2376-59922019-09-015e21910.7717/peerj-cs.219The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic reviewAna Reyes-Menendez0Jose Ramon Saura1Ferrão Filipe2Department of Business Economics, Rey Juan Carlos University, Madrid, SpainDepartment of Business Economics, Rey Juan Carlos University, Madrid, SpainVice-Rector Universidade Portucalense, Universidade Portucalense Infante D. Henrique, Porto, PortugalIn 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.https://peerj.com/articles/cs-219.pdfOnline reviewsFake reviewsConsumer behaviorAlgorithmsTourism |
| spellingShingle | Ana Reyes-Menendez Jose Ramon Saura Ferrão Filipe The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review PeerJ Computer Science Online reviews Fake reviews Consumer behavior Algorithms Tourism |
| title | The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review |
| title_full | The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review |
| title_fullStr | The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review |
| title_full_unstemmed | The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review |
| title_short | The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review |
| title_sort | importance of behavioral data to identify online fake reviews for tourism businesses a systematic review |
| topic | Online reviews Fake reviews Consumer behavior Algorithms Tourism |
| url | https://peerj.com/articles/cs-219.pdf |
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