Abstract
BackgroundOnline consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. The problem being solved is one of the characteristics that lead to fraud rather than detecting fraud.MethodsNeural network algorithm is used to evaluate the proposed feature set and compare it against the state-of-the-art feature sets in detecting fraud. The feature set considers the user’s social interaction on the Yelp platform to determine if the user is committing fraud. The neural network algorithm helps in comparing the feature set with other feature sets used to detect fraud. Any attempt to find the characteristics that lead to fraud has a prerequisite to be good enough to detect fraud as well.ResultsThe F1 score obtained using neural networks is on par with all the well-known methods for detecting fraud, a value of 0.95. The effectiveness of the feature set is in rivaling the other approaches to fraud detection.ConclusionsA user’s social interaction on a digital platform such as Yelp is equally important in evaluating the user as social interaction is in real life. The characteristics that lead to fraud can be intuitively captured. The characteristics such as number of friends, number of followers and the number of times the user has provided a review which was helpful to multiple people provide the neural network with a base to form a relationship between opinion fraud and social interaction characteristics.
Highlights
Online consumer reviews have become a baseline for new consumers to try out a business or a new product
The experimental results show that there is some improvement in the performance of the neural network algorithm as we add on user interaction features
The results obtained from it are equivalent to the ones we obtain using user behavioral features. This could only indicate that the neural network is able to discern a relationship between the social interaction features and opinion fraud
Summary
Online consumer reviews have become a baseline for new consumers to try out a business or a new product. As online-shopping, e-commerce and social networks are ubiquitous and become an integral part of our daily lives, these reviews have direct influence on product and business sales [7, 26] Since such user-generated content contains rich information about user experiences and opinions, it is useful for potential customers to make better. The financial benefits reaped from such fake reviews have even created a market of paid users They are paid to counterfeit fake reviews either to fabricate hype to promote a business or to tear down competitive products or businesses. This could be collectively identified as opinion fraud or opinion spam
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