Banerjee, Snehasish orcid.org/0000-0001-6355-0470, Chua, Alton Y.K. and Kim, Jung-Jae (2017) Don’t be deceived:Using linguistic analysis to learn how to discern online review authenticity. Journal of the Association for Information Science and Technology. pp. 1525-1538. ISSN 2330-1643
Abstract
This article uses linguistic analysis to help users discern the authenticity of online reviews. Two related studies were conducted using hotel reviews as the test case for investigation. The first study analyzed 1,800 authentic and fictitious reviews based on the linguistic cues of comprehensibility, specificity, exaggeration, and negligence. The analysis involved classification algorithms followed by feature selection and statistical tests. A filtered set of variables that helped discern review authenticity was identified. The second study incorporated these variables to develop a guideline that aimed to inform humans how to distinguish between authentic and fictitious reviews. The guideline was used as an intervention in an experimental setup that involved 240 participants. The intervention improved human ability to identify fictitious reviews amid authentic ones.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 ASIS&T. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > The York Management School |
Depositing User: | Pure (York) |
Date Deposited: | 08 Dec 2017 12:40 |
Last Modified: | 17 Mar 2025 00:07 |
Published Version: | https://doi.org/10.1002/asi.23784 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1002/asi.23784 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125055 |