Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity.
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...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 68; no. 6; pp. 1525 - 1539 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123088785&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123088785 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Jun2017 vid: 68 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 123088785 123088785 123088785 10.1002/asi.23784 123088785 ppf: 1525 ppct: 14 formats: tig: atl: Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity. aug: au: Banerjee, Snehasish Chua, Alton Y. K. Kim, Jung‐Jae affil: Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, 637718, Singapore sug: subj: Linguistics Utilization Deception Internet Public Opinion Hotels Cues Algorithms Human Data Analysis Trust Unpaired T-Tests Consumer Attitudes ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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