Identifying Manipulated Offerings on Review Portals
Jiwei Li, Myle Ott, Claire Cardie · 2013
Recent work has developed supervised methods for detecting deceptive opinion spamfake reviews written to sound authentic and deliberately mislead readers.And whereas past work has focused on identifying individual fake reviews, this paper aims to identify offerings (e.g., hotels) that contain fake reviews.We introduce a semi-supervised manifold ranking algorithm for this task, which relies on a small set of labeled individual reviews for training.Then, in the absence of gold standard labels (at an offering level), we introduce a novel evaluation procedure that ranks artificial instances of real offerings, where each artificial offering contains a known number of injected deceptive reviews.Experiments on a novel dataset of hotel reviews show that the proposed method outperforms state-of-art learning baselines.