SURVEY ON REVIEW SPAM DETECTION

The proliferation of E-commerce sites has made web an excellent source of gathering customer reviews about products; as there is no quality control anyone one can write anything which leads to review spam. This paper previews and reviews the substantial research on Review Spam detection technique. Further it provides state of art depicting some previous attempt to study review spam detection

Detecting Spam Review through Spammer’s Behavior Analysis

Online reviews about the purchase of a product or services provided have become the main source of user opinions. To gain profit or fame usually spam reviews are written to promote or demote some target products or services. This practice is known as review spamming. In the last few years, different methods have been suggested to solve the problem of review spamming but there is still a need to introduce new spam review detection method to improve accuracy results. In this work, researchers have studied six different spammer behavioral features and analyzed the proposed spam review detection method using weight method. An experimental evaluation was conducted on a benchmark dataset and achieved 84.5% accuracy

Review Spam Detection Using Machine Learning Techniques

Nowadays with the increasing popularity of internet, online marketing is going to become more and more popular. This is because, a lot of products and services are easily available online. Hence, reviews about these all products and services are very important for customers as well as organizations. Unfortunately, driven by the will for profit or promotion, fraudsters used to produce fake reviews. These fake reviews written by fraudsters prevent customers and organizations reaching actual conclusions about the products. Hence, fake reviews or review spam must be detected and eliminated so as to prevent deceptive potential customers. In our work, supervised and semi-supervised learning technique have been applied to detect review spam. The most apt data sets in the research area of review spam detection has been used in proposed work. For supervised learning, we try to obtain some feature sets from different automated approaches such as LIWC, POS Tagging, N-gram etc., that can best distinguish the spam and non-spam reviews. Along with these features sentiment analysis, data mining and opinion mining technique have also been applied. For semi-supervised learning, PU-learning algorithm is being used along with six different classifiers (Decision Tree, Naive Bayes, Support Vector Machine, k-Nearest Neighbor, Random Forest, Logistic Regression) to detect review spam from the available data set. Finally, a comparison of proposed technique with some existing review spam detection techniques has been done

Network Spam To Create A Arrangement Intended For Online Public Reviews

The ability for anyone to leave a comment offers a golden opportunity for spammers to write spam reviews of products and services for a variety of interests. Using the importance of spam functions helps us perform better in terms of various metrics tested on real-world review data sets from Yelp and Amazon. Identifying spammers and spam is a hot topic of research, and although a large number of studies have recently been conducted for this purpose, the methodologies presented so far barely detect spam reviews and none have demonstrated the importance of each type of extracted feature. . In this study, we propose a new framework, called Network Spam that uses spam properties to model review data sets as heterogeneous information networks to assign a spam detection procedure to the classification problem in those networks. The results show that the spam network outperforms existing methods and four classes of characteristics; including behavior review, user behavior, language review, user language, and the first type of features work better than other categories

Online Review Spam Detection by New Linguistic Features

With the fast growing and importance of online reviews, malicious users start to abuse the online review websites and deliberately post low quality, untrustworthy, or even fraudulent reviews, which are typically referred to as ``spam reviews''. Many existing studies on review spam detection are based on classification models. Features such as the number of verbs used in the reviews are commonly used to construct the spam review classification model. Surprisingly, many linguistic features of users' reviews have not been thoroughly considered for review spam detection. In this paper, we focus on different types of linguistic features and evaluate their performance on detecting spam reviews. Our empirical evaluation conducted on a spam review benchmark dataset validated the proposed features significantly improve the performance of online review spam detection, reaching more than 93\% accuracy.ye

Detecting Review Spam: Challenges and Opportunities

Abstract-Online customer reviews for both products or merchants have greatly affected others' decision making in purchase. Considering the easily accessibility of the reviews and the significant impacts to the retailers, there is an increasing incentive to manipulate the reviews, mostly profit driven. Without proper protection, spam reviews will cause gradual loss of credibility of the reviews and corrupt the entire online review systems eventually. Therefore, review spam detection is considered as the first step towards securing the online review systems. In this paper, we aim to overview existing detection approaches in a systematic way, define key research issues, and articulate future research challenges and opportunities for review spam detection. Index Terms-Review spam, review spammer, spam behav ior

Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network

Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms. In this work, we use CNN model to classify image spam respectively whereas the post-hoc XAI methods including Local Interpretable Model Agnostic Explanation (LIME) and Shapley Additive Explanations (SHAP) were deployed to provide explanations for the decisions that the black-box CNN models made about spam image detection. We train and then evaluate the performance of the proposed approach on a 6636 image dataset including spam images and normal images collected from three different publicly available email corpora. The experimental results show that the proposed framework achieved satisfactory detection results in terms of different performance metrics whereas the model-independent XAI algorithms could provide explanations for the decisions of different models which could be utilized for comparison for the future study.Comment: Under review by International Conference on Cyber Resilience (ICCR), Dubai 202

A Review on mobile SMS Spam filtering techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement