Detecting Fake Reviews: Just a Matter of Data

Along with the ever-increasing portfolio of products online, the incentive for market participants to write fake reviews to gain a competitive edge has increased as well. This article demonstrates the effectiveness of using different combinations of spam detection features to detect fake reviews other than the review-based features typically used. Using a spectrum of feature sets offers greater accuracy in identifying fake reviews than using review-based features only, and using a machine learning algorithm for classification and different amounts of feature sets further elucidates the difference in performance. Results compared by benchmarking show that applying a technique prioritizing feature importance benefits from prioritizing features from multiple feature sets and that creating feature sets based on reviews, reviewers and product data can achieve the greatest accuracy

Man vs machine – Detecting deception in online reviews

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material