Using supervised learning to classify authentic and fake online reviews

Snehasish Banerjee, Alton Y.K. Chua, Jung-Jae Kim

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Number of pages8
Publication statusPublished - 2015
EventInternational Conference on Ubiquitous Information Management and Communication - Bali, Indonesia
Duration: 8 Jan 201510 Jan 2015


ConferenceInternational Conference on Ubiquitous Information Management and Communication
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