Quantifier Guided Aggregation for the Veracity Assessment of Online Reviews

The Social Web is characterized by a massive diffusion of unfiltered content, directly generated by users via the spread of different social media platforms. In this context, a challenging issue is to assess the veracity of the information generated within the sites of online reviews. To address this issue, a common practice in the literature is to select and analyze some veracity features associated with users and their reviews, by mostly applying machine learning techniques, to provide a classification in genuine and deceptive reviews. In this paper, we do not focus on the feature selection and user behavior analysis issues, but we concentrate on the aggregation process with respect to each single veracity feature. In most of the approaches based on machine learning techniques, the contribution of each feature in the classification process is not measurable by the user. For this reason, we propose a multicriteria decision making approach based both on the assessment of multiple criteria and the use of aggregation operators with the aim of obtaining a veracity score associated with each review. Based on this score, it is possible to detect fake reviews. The proposed model is evaluated on a Yelp data set by applying different aggregation schemes, and it is compared with well-known supervised machine learning techniques.