Man vs machine – Detecting deception in online reviews

[Display omitted] •Differences in authenticity, analytical style, sentiment, and social orientation.•Deception incorporates information manipulation and self-presentation elements.•Text analysis methods have beneficial complementary aspects for deception analysis.•Deception analysis should consider quantity, quality, relevance, and context.•Readability indices provide information about the relevance and style of 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.