Man vs machine - Detecting deception in online reviews

被引：12

作者：

Petrescu, Maria ^{[1
]}

Ajjan, Haya ^{[2
]}

Harrison, Dana L. ^{[3
]}

机构：

[1] Embry Riddle Aeronaut Univ, Daytona Beach, FL 32114 USA

[2] Elon Univ, Elon, NC USA

[3] East Tennessee State Univ, Johnson City, TN USA

来源：

JOURNAL OF BUSINESS RESEARCH | 2023年 / 154卷

关键词：

INFORMATION MANIPULATION; SELF-PRESENTATION; FAKE REVIEWS; ASSISTING CONSUMERS; TEXT ANALYSIS; PERCEPTIONS; BUSINESS; WORDS;

D O I：

10.1016/j.jbusres.2022.113346

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页数：14

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