Using supervised learning to classify authentic and fake online reviews
Date of Issue
2015Conference Name
9th International Conference on Ubiquitous Information Management and Communication
School
Wee Kim Wee School of Communication and Information
Version
Published version
Abstract
Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.
Subject
DRNTU::Library and information science::Libraries::Information systems
DRNTU::Social sciences::Communication::Communication theories and models
DRNTU::Social sciences::Communication::Communication theories and models
Type
Conference Paper
Rights
© 2015 Association for Computing Machinery. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, Association for Computing Machinery. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2701126.2701130].
Collections
http://dx.doi.org/10.1145/2701126.2701130
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