Let’s vote to classify authentic and manipulative online reviews: The role of comprehensibility, informativeness and writing style

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

Research output: Contribution to conferencePaperpeer-review


Scholars increasingly seek to investigate differences between authentic and manipulative online reviews. A common line of research argues that authentic and manipulative reviews are distinguishable based on three textual characteristics, namely, comprehensibility, informativeness and writing style. Although recent studies have analyzed differences between authentic and manipulative reviews in terms of these textual characteristics, they often lack in terms of methodological rigor. For one, datasets used for analysis are not always representative. Moreover, only few machine learning algorithms are used to classify authentic and manipulative reviews. Recognizing the value of methodological rigor, this paper extends prior studies by examining textual differences between authentic and manipulative reviews using a more representative dataset. Moreover, authentic and manipulative reviews were classified using a voting among multiple classifiers that had been used in recent literature. The implications of the results are discussed.
Original languageEnglish
Number of pages7
Publication statusPublished - 2015
EventScience and Information Conference - London, United Kingdom
Duration: 28 Jul 201530 Jul 2015


ConferenceScience and Information Conference
Country/TerritoryUnited Kingdom
Internet address

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