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      Using supervised learning to classify authentic and fake online reviews

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      a88-banerjee.pdf (263.0Kb)
      Author
      Banerjee, Snehasish
      Chua, Alton Yeow Kuan
      Kim, Jung-Jae
      Date of Issue
      2015
      Conference 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
      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].
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      • WKWSCI Conference Papers
      http://dx.doi.org/10.1145/2701126.2701130
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