Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

Abstract

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammer's behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers' behavioral features and linguistic method utilizing different available classifiers. INDEX TERMS Online product reviews, spam reviews, spam review detection, linguistic features, spammer behavioral features.

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

“…(3), it gives a value of almost zero when the distance goes beyond 10 (Figure 2). Thus, to solve this issue, the distances between the agents are projected to the range [1,4]. The G i component in Eq.…”

“…The GOA algorithm starts by generating a random population Update the parameter c using Eq. (8); for each grasshopper in population Normalize the distances between grasshoppers in X d i to [1], [4]; Update x ∈ X d i by using Eq. (7); Adjust the boundaries for the current grasshopper in population; end for Update T if there is a better solution; iter = iter + 1; end while Return the best solution of T; grasshopper (solution) x d , d = 1, 2, .…”

“…, dim, and the best grasshopper (solution), T d , is then selected according to the best fitness function. Thereafter, for each grasshopper x i of the population, the following three steps are implemented: 1) Normalizing the distances between grasshoppers in X d i to [1], [4]; 2) Update the current grasshopper x i ∈ X d i by using Eq. (7); 3) Adjusting the boundaries for the current grasshopper in the population.…”

“…Email is considered one of the main methods for information dissemination around the world. It is fast, efficient, inexpensive, and easy to use via smartphones, laptops, desktops, and other latest-generation electronic devices that have made emails most popular [1], [2]. Despite the prosperity in the use of other means of communication over the Internet, such as social networks and instant messaging, etc., emails have been kept the leadership in social, academic, and business communications and remain a prerequisite for other means of communication and electronic transactions.…”

Spammer group detection and diversification of customers’ reviews

“…Therefore, collective behaviors of a group working together on several products can be difficult to identify the spam reviews. Most of the existing studies have identified spam reviews by utilizing the individual spammer behavioral features (Cao et al, 2019;Hussain et al, 2020;Rahman et al, 2015).…”