Detecting Product Review Spammers Using Principles of Big Data

Abstract

Growing consumerism has led to the importance of online reviews on the Internet. Opinions voiced by these reviews are taken into consideration by many consumers for making financial decisions online. This has led to the development of opinion spamming for profitable motives or otherwise. Work has been done to tackle the challenge of identifying such spammers, but the scale of the real-world review systems demands this problem to be tackled as a Big Data challenge. So, an effort has been made to detect online review spammers using the principle of Big Data. In this work, a rating-based model has been studied under the light of large-scale datasets (more than 80 million reviews by 20 million reviewers) using the Hadoop and Spark frameworks. Scale effects have been identified and mitigated to provide better context to large review systems. An improved computational framework has been presented to compute the overall spamcity of reviewers using exponential smoothing. The value of the smoothing factor was set empirically. Finally, future directions have been discussed.

Identifying and Profiling Radical Reviewer Collectives in Digital Product Reviews

“…It has also caused opinion spamming for fame and profits. Rout et al [18] conducted a study to identify those spammers with big data. They studied a "rating-based model" with large datasets of over "80 million reviews posted by 20 million reviewers through Spark and Hadoop frameworks.…”

“…2 illustrates the proposed model based on big data to detect extremist reviews by Rout et al [18]. Rout et al [18] presented a "metadata based modeling based on large-scale reviewing with the help of big data. Fig.…”

Fake Review Identification Methods Based on Multidimensional Feature Engineering

“…In order to obtain more business bene ts from the reviews, especially praise, many businesses began to exploit some loopholes or mechanisms in the platforms to write some fake reviews to cheat consumers or ght competitors [6][7][8]. Reviews with this deception or fraudulent behavior are spam reviews or fake reviews [9].…”