Banerjee, S., Chua, A. Y. K. and Kim, J.-J. (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, 68: 1525–1538. doi: 10.1002/asi.23784
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.
License type:
PublisherCopyrights
Funding Info:
This work was supported by the Ministry of Education Research Grant AcRF Tier 2 (MOE2014-T2-2-020).