Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107209
Title: Using supervised learning to classify authentic and fake online reviews
Authors: Banerjee, Snehasish
Chua, Alton Yeow Kuan
Kim, Jung-Jae
Keywords: DRNTU::Library and information science::Libraries::Information systems
DRNTU::Social sciences::Communication::Communication theories and models
Issue Date: 2015
Source: Banerjee, S., Chua, A. Y. K., & Kim, J.-J. (2015). Using supervised learning to classify authentic and fake online reviews. Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication.
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.
URI: https://hdl.handle.net/10356/107209
http://hdl.handle.net/10220/25330
DOI: 10.1145/2701126.2701130
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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:WKWSCI Conference Papers

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