Characterizing the dynamics and evolution of incentivized online reviews on Amazon


During the past few years, sellers have increasingly offered discounted or free products to selected reviewers of e-commerce platforms in exchange for their reviews. Such incentivized (and often very positive) reviews can improve the rating of a product which in turn sways other users’ opinions about the product. Despite their importance, the prevalence, characteristics, and the influence of incentivized reviews in a major e-commerce platform have not been systematically and quantitatively studied. This paper examines the problem of detecting and characterizing incentivized reviews in two primary categories of Amazon products. We describe a new method to identify explicitly incentivized reviews (EIRs) and then collect a few datasets to capture an extensive collection of EIRs along with their associated products and reviewers. We show that the key features of EIRs and normal reviews exhibit different characteristics. Furthermore, we illustrate how the prevalence of EIRs has evolved and been affected by Amazon’s ban. Our examination of the temporal pattern of submitted reviews for sample products reveals promotional campaigns by the corresponding sellers and their effectiveness in attracting other users. We also demonstrate that a classifier that is trained by EIRs (without explicit keywords) and normal reviews can accurately detect other EIRs as well as implicitly incentivized reviews. Finally, we explore the current state of explicit reviews on Amazon. Overall, this analysis sheds insightful light on the impact of EIRs on Amazon products and users.

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    Our manual inspection process was conducted in multiple rounds as follows: We first select all the reviews that contain our target keywords (e.g., free, discount) to create a pool. Then, we select 100 random samples of reviews from this pool to manually inspect in each round. As EIRs tend to contain some variants of the same disclaimer sentence, our manual inspection quickly identifies such signatures and uses them to automatically identify reviews in the pool that contain similar signatures. The examination of these reviews also reveals false alarm cases.

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    Amazon provides the date when a product becomes available for some categories of product. However, we frequently observe cases where a product has multiple versions in the same product page that have become available at different times but share the same pool of reviews. We use the time between the first and last reviews across all versions of a product to deal with this ambiguity in relating specific review to a particular version of a product.

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    Amazon appears to rely on some weighted averaging method (Bishop 2015) to calculate the overall rating of a product based on factors such as the recency of a review, its helpfulness and whether it is associated with a verified purchase. Since the details of Amazon’s rating method are unknown, we simply rely on a linear moving average of all ratings to determine the overall rating of each product or reviewer over time.

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    We consider character-based n-grams since they are shown to be more robust as they capture spelling differences (Kanaris et al. 2007) and are more effective in authorship attribution (writer identification) (Koppel et al. 2011) as they cover a little bit of lexical content, syntactic content, and even style by covering punctuation and white spaces.


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This material is based upon work supported by the National Science Foundation under Grants Nos. CNS-1564348 and CHS-1551817. We gratefully acknowledge the support of Intel Corporation for giving access to the Intel AI DevCloud platform used for this work.

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Correspondence to Soheil Jamshidi.

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Jamshidi, S., Rejaie, R. & Li, J. Characterizing the dynamics and evolution of incentivized online reviews on Amazon. Soc. Netw. Anal. Min. 9, 22 (2019).

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  • Incentivized online reviews
  • Machine learning
  • Modeling
  • Amazon
  • Online review