Contrasting Fake Reviews in TripAdvisor.

Fake reviews are a concrete problem still affecting the reliability of systems like TripAdvisor, especially in the case of few reviews, thus in the first, most vulnerable, activity period of operators. In this work-in-progress paper, we present a model aimed to contrast this problem, based on a sort of normalization of scores given by users, to take into account the level of assurance of the reviews. This is done by considering two different dimensions, combined each other, which are the level of assurance of the identity of the review’s author and the level of assurance of the occurrence of the evaluated experience. The paper presents a first validation of the approach conducted on real-life data, giving us very encouraging results.