Abstract : Most adults in industrialized countries now routinely check online reviews before selecting a product or service such as lodging. This reliance on online reviews can entice some hotel managers to pay for fraudulent reviews – either to boost their own property or to disparage their competitors. The detection of fraudulent reviews has been addressed by humans and by machine learning approaches yet remains a challenge. We conduct an empirical study in which we create fake reviews, merge them with verified reviews and then employ four methods (Naïve Bayes, SVMs, human computation and hybrid human-machine approaches) to discriminate the genuine reviews from the false ones. We find that overall a hybrid human-machine method works better than either human or machine-based methods for detecting fraud – provided the most salient features are chosen. Our process has implications for fraud detection across numerous domains, such as financial statements, insurance claims, and reporting clinical trials.
https://hal.inria.fr/hal-02510146
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Christopher Harris. Comparing Human Computation, Machine, and Hybrid Methods for Detecting Hotel Review Spam. 18th Conference on e-Business, e-Services and e-Society (I3E), Sep 2019, Trondheim, Norway. pp.75-86, ⟨10.1007/978-3-030-29374-1_7⟩. ⟨hal-02510146⟩