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- Fake review detection (2)
- Deception cues (1)
- Fake review cues (1)
- Human review fraud detection (1)
- Opinion scam (1)
- Review scam (1)
- User-perspective (1)
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Background
Consumers rely heavily on online user reviews when shopping online and cybercriminals produce fake reviews to manipulate consumer opinion. Much prior research focuses on the automated detection of these fake reviews, which are far from perfect. Therefore, consumers must be able to detect fake reviews on their own. In this study we survey the research examining how consumers detect fake reviews online.
Methods
We conducted a systematic literature review over the research on fake review detection from the consumer-perspective. We included academic literature giving new empirical data. We provide a narrative synthesis comparing the theories, methods and outcomes used across studies to identify how consumers detect fake reviews online.
Results
We found only 15 articles that met our inclusion criteria. We classify the most often used cues identified into five categories which were (1) review characteristics (2) textual characteristics (3) reviewer characteristics (4) seller characteristics and (5) characteristics of the platform where the review is displayed.
Discussion
We find that theory is applied inconsistently across studies and that cues to deception are often identified in isolation without any unifying theoretical framework. Consequently, we discuss how such a theoretical framework could be developed.
Consumers are using online reviews to decide which products to purchase. Cybercriminals produce fake reviews to influence unknowing consumers into buying products of lower quality, which can lead to financial, emotional and physical damage. We have little under-standing of how consumers make decisions about the veracity of online reviews, or incorpo-rate online reviews into purchasing decisions, especially outside of the laboratory. Therefore, in this study using a grounded theory approach we elaborate on how consumers determine the veracity and trustworthiness of online user reviews. Twenty-five interviews with Dutch and German consumers were held to identify deception cues, thought processes and other markers of online shopping behaviour. The results show that consumers use online reviews differently depending on context. Our new theory proposes that consumers process reviews in at least two steps. First, they scan the review for relevance and then they determine the trustworthiness, credibility, and veracity. Additionally, we identified different deception cues that are used. Together, these findings lead the way into a new understanding of human fake review detection online.
