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.

Automated summary

This study aims to analyze methods for identifying deceptive online consumer reviews, evaluate insights from automated approaches using individual and aggregated review data, and formulate a review interpretation framework for deception detection. The findings demonstrate the interchangeable characteristics of different automated text analysis methods and highlight their complementary aspects. Employing an integrative multi-method model at both the individual and aggregate level provides more comprehensive insights regarding review information, sentiment, relevance, context, and cognitive aspects.