Software Engineering: Creating an Email Notification System for Amazon Sellers; An STS Perspective on Amazon's Fake Review Problem

Wang, Jeffrey, School of Engineering and Applied Science, University of Virginia
Graham, Daniel, EN-Comp Science Dept, University of Virginia
Baritaud, Catherine, EN-Engineering and Society, University of Virginia

Today, is the world’s biggest e-commerce website, which is powered by a large community of third-party sellers. Amazon provides an online marketplace specifically tailored to their sellers, but this marketplace is lacking in user reviews. As such, the technical research report aims to increase user engagement in this marketplace through the creation of an email notification system that sends reminders to leave reviews. Although Amazon can encourage people to leave reviews on their website, Amazon is not able to guarantee that user reviews are genuine. This predicament leads to the STS research paper, which investigates the fake review problem that has plagued Amazon over the past decade. The tightly coupled technical and STS topics both revolve around user reviews on Amazon, in which they examine how a review system is supplemented with a notification system, as well as the various social groups that influence the design and use of a review system.

The technical research report outlines the process of creating a new email notification system that works with Amazon’s Seller Central Partner Network (SCPN). After a seller subscribes to an application on the SCPN, they will receive an email reminder to leave a review for that application. In order to determine who needs to receive an email reminder, the notification system queries several internal Amazon databases and sends over the relevant data to an AWS Lambda function, which is a cloud computing service provided by Amazon. If a seller is eligible to leave a review, then the notification system sends the email reminder and keeps track of that seller in a DynamoDB database.

Completion of this notification system was achieved over a twelve week internship at Amazon. A finalized design of the system was presented after the first six weeks, and the remainder of the internship was dedicated to the implementation of the system. The notification system is able to receive new seller data every day and successfully sends an email to the target seller. Due to time constraints, not all the features proposed in the design were able to be implemented. After the end of the internship, the notification system was passed on to Amazon’s SCPN software engineering team, where they are continuing to add functionality to the system.

The initial inspiration for researching Amazon’s fake review problem came from a desire to understand the social context of online misinformation, and the focus was placed on Amazon to provide an appropriate scope of topic that is closely related to the technical project. When it comes to Amazon, the STS research strived to answer the question of why people are motivated leave fake reviews and how different social groups influenced Amazon’s decisions to revise their review system. Pinch and Bijker’s Social Construction of Technology theory was the framework used to analyze Amazon’s fake review problem. This framework was best suited to explain how Amazon’s review system is constantly evolving in order to meet the demands of each social group.

As it turns out, a major contribution to Amazon’s fake review problem involves incentivized reviews. Although Amazon decided to ban incentivized reviews on their website, nefarious sellers discovered that they could fish for fake reviews on other platforms such as Facebook. Thus, Amazon’s fake review problem persisted, and the company resorted to additional measures to attempt to contain the damage caused by fake reviews. Although the STS research did not recommend a solution to Amazon’s fake review problem, the research instead provided a better understanding and appreciation for how individuals can shape a particular technology.

BS (Bachelor of Science)
Social Construction of Technology, Amazon, E-commerce, Fake reviews

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Daniel Graham
STS Advisor: Catherine Baritaud

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