Spam Reviews Research Articles

Early Reviewers Prediction and Spammer Detection on E-Commerce Websites

Reviews which are posted online play a vital part in present world as most of the customer's purchase items through an e-commerce website. Reviews which are posted on websites at an early stage known as early reviews, even though their contribution is very small their opinions determine new product's success and failure. Most of the spam reviews are written to improve their profit and promote their products and defame other products. In this system, the concentration is mainly on early reviews of the products and the products categories ranking on e-commerce websites i.e., Amazon. The analysis of reviews of product defines ratings of early reviewers’ and helpfulness scores of them are probably influencing product promotion additionally this model is enhanced with ranking and spammer detection.

Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network

Opinion mining and sentiment analysis are useful to extract subjective information out of bulk text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. Though performing sentiment analysis is a challenging task for the researchers to identify the user’s sentiments from the large datasets, it is unstructured in nature, and also includes slangs, misspells, and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; they are data collection, pre-processing, keyword extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, pre-processing was carried out for enhancing the quality of collected data. The pre-processing phase comprises of three systems: lemmatization, review spam detection, and removal of stop words and URLs. Then, an effective topic modelling approach latent Dirichlet allocation along with modified possibilistic fuzzy C-Means was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative, and neutral) by applying an effective machine learning classifier: Selective memory architecture-based convolutional neural network. The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6–20% related to the existing systems.

An Effective System to Detect Fake Research

Detection of spam review is an important operation for present e-commwebsites and apps.We address the issue on fake review detection in user reviews in e-commerce application, which wasimportant for implementing anti-opinion spam.First we analyze the characteristics of fake reviews and we apply the machine learning algorithms on that data. Spam or fake reviews of the itemsreducing the reliability of decision making and competitive analysis.The presence of fake reviews makes the customer cannot make the right decisions of sellers, which can also causes the goodwill of the platform decreased. There is a chance of leaving appraisals via web-based networking media systems whether states or harming by spammers on specific item, firm alongside their answers by recognizing these spammers just as in like manner spams so as to understand the assessments in the interpersonal organizations sites, we exist a stand-out structure called Netspam which uses spam highlights for demonstrating tribute datasets as heterogeneous subtleties systems to guide spam location treatment directly into gathering issue in such systems.

An ensemble approach for spam detection in Arabic opinion texts

Nowadays, individuals express experiences and opinions through online reviews. These has an influence on online marketing and obtaining real knowledge about products and services. However, some of the online reviews can be unreal. They may have been written to promote low-quality products/services or sabotage a product/service reputation to mislead potential customers. Such misleading reviews are known as spam reviews and require crucial attention. Prior spam detection research focused on English reviews, with less attention to other languages. The detection of spam reviews in Arabic online sources is a relatively new topic despite the relatively huge amount of data generated. Therefore, this paper contributes to such topic by presenting four different Arabic spam reviews detection methods, while putting more focus towards the construction and evaluation of an ensemble approach. The proposed ensemble method is based on integrating a rule-based classifier with machine learning techniques, while utilizing content-based features that depend on N-gram features and Negation handling. The four proposed methods are evaluated on two datasets of different sizes. The results indicate the efficiency of the ensemble approach where it achieves a classification accuracy of 95.25% and 99.98% for the two experimented datasets and outperforming existing related work by far of 25%.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

Integrating aspect analysis and local outlier factor for intelligent review spam detection

Recently, online reviews are used increasingly by individuals and organizations for making purchase and business decisions. Unfortunately, driven by profit and fame, spammers post spurious reviews to mislead the customers. Therefore, intelligent detection of the spam reviews from a large scale of texts is a great challenge. This paper describes an unsupervised method aiming for intelligently detecting online review spams. Review spam detection is transformed into a density-based outlier detection problem. The proposed method generates a sentiment lexicon to calculate the aspect rating of reviews,and proposes an aspect-rating local outlier factor model (AR-LOF) to identify the spam reviews. The experiments on TripAdvisor demonstrate the high effectiveness and intelligence of the proposed model, which has the potential to significantly help the online web business.

Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph

Spam reviews typically appear perfectly normal until examined in a large context. The standard approach to classifying reviews independently ignores these relations. In this study, we propose a complex probabilistic graph classification approach to address the problem of opinion spam detection. To obtain an initial effective spamicity estimation for the nodes (reviews, authors, and products) in the graph, we first train a neural network with attention mechanism to learn the multimodal embedded representation of nodes by leveraging both textual and rich features. Then based on the node prior computation, a heterogeneous graph is constructed to capture the relationships among different kinds of nodes, and the beliefs are further updated through iterative message propagation. To support this work, we collect two kinds of real-life datasets, which are separately composed of 97,839 restaurant reviews and 31,317 hotel reviews. The evaluation of the two datasets demonstrates the effectiveness of the proposed approach. We further analyze several salient rich features and the intermediate component of our model, thereby revealing that their states capture certain statistical characteristics of the datasets.

Survey on Spam Reviews and Spammer Community Detection Technique

Survey on Spam Reviews and Spammer Community Detection Technique

A Review Structure Based Ensemble Model for Deceptive Review Spam

Consumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features of spam reviews. In this paper, we propose a novel model, which splits a review into three parts: first sentence, middle context, and last sentence, based on the discovery that the first and last sentence express stronger emotion than the middle context. Then, the model uses four independent bidirectional long-short term memory (LSTM) models to encode the beginning, middle, end of a review and the whole review into four document representations. After that, the four representations are integrated into one document representation by a self-attention mechanism layer and an attention mechanism layer. Based on three domain datasets, the results of in-domain and mix-domain experiments show that our proposed method performs better than the compared methods.

Content-Aware Trust Propagation Toward Online Review Spam Detection

With the increasing popularity of online review systems, a large volume of user-generated content becomes available to help people make reasonable judgments about the quality of services and products from unknown providers. However, these platforms are frequently abused since fraudulent information can be freely inserted by potentially malicious users without validation. Consequently, online review systems become targets of individual and professional spammers, who insert deceptive reviews by manipulating the rating and/or the content of the reviews. In this work, we propose a review spamming detection scheme based on the deviation between the aspect-specific opinions extracted from individual reviews and the aggregated opinions on the corresponding aspects. In particular, we model the influence on the trustworthiness of the user due to his opinion deviations from the majority in the form of a deviation-based penalty, and integrate this penalty into a three-layer trust propagation framework to iteratively compute the trust scores for users, reviews, and review targets, respectively. The trust scores are effective indicators of spammers, since they reflect the overall deviation of a user from the aggregated aspect-specific opinions across all targets and all aspects. Experiments on the dataset collected from Yelp.com show that the proposed detection scheme based on aspect-specific content-aware trust propagation is able to measure users’ trustworthiness based on opinions expressed in reviews.

Opinion spam detection framework using hybrid classification scheme

With the advent of social networking sites, opinion-mining applications have attracted the interest of the online community on review sites to know about products for their purchase decisions. However, due to increasing trend of posting spam (fake) reviews to promote the target products or defame the specific brands of competitors, Opinion Spam detection and classification has emerged as a hot issue in the community of opinion mining and sentiment analysis. We investigate the issue of Opinion Spam detection by using different combinations of entities, features, and their sentiment scores. We enrich the feature set of a baseline Spam detection method with Spam detection features (Opinion Spam, Opinion Spammer, Item Spam). Using a dataset of reviews from the Amazon site and sentences labeled for Spam detection, we evaluate the role of spamicity-related features in detecting and classifying spam (fake) clues and distinguishing them from genuine reviews. For this purpose, we introduce a rule-based feature weighting scheme and propose a method for tagging the review sentence as spam and non-spam. Experiments results depict that spam-related features improve Spam detection in review sentences posted on product review sites. Adding a revised feature weighting scheme achieved an accuracy increase from 93 to 96%. Furthermore, a hybrid set of features are shown to improve the performance of Opinion Spam detection in terms of better precision, recall, and F-measure values. This work shows that combining spam-related features with rule-based weighting scheme can improve the performance of even baseline Spam detection method. This improvement can be of use to Opinion Spam detection systems, due to the growing interest of individuals and companies in isolating fake (spam) and genuine (non-spam) reviews about products. The outcome of this work will provide an insight into spam-related features and feature weighting and will assist in developing more advanced applications for Opinion Spam detection. In the field of Opinion Spam detection, previous state-of-the-art studies used less number of spamicity-related features and less efficient feature weighting scheme. However, we provided a revised feature selection and a revised feature weighting scheme with normalized spamicity score computation technique. Therefore, our contribution is novel to the field because it provides a significant improvement over the comparing methods.

Improved Text Mining Techniques for Spam Review Detection

Improved Text Mining Techniques for Spam Review Detection

Opinion Spam Detection based on Annotation Extension and Neural Networks

Online reviews play an increasingly important role in the purchase decisions of potential customers. Incidentally, driven by the desire to gain profit or publicity, spammers may be hired to write fake reviews and promote or demote the reputation of products or services. Correspondingly, opinion spam detection has attracted attention from both business and research communities in recent years. However, unlike other tasks such as news classification or blog classification, the existing review spam datasets are typically limited due to the expensiveness of human annotation, which may further affect detection performance even if excellent classifiers have been developed. We propose a novel approach in this paper to boost opinion spam detection performance by fully utilizing the existing labelled small-size dataset. We first design an annotation extension scheme that uses extra tree classifiers to train multiple estimators and then iteratively generate reliable labelled samples from unlabeled ones. Subsequently, we examine neural network scenarios on a newly extended dataset to learn the distributed representation. Experimental results suggest that the proposed approach has better generalization capability and improved performance than state-of-the-art methods.

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.

Opinion Spam Detection and Analysis by Identifying Domain Features in Product Reviews

With the rapid advent of technology there is an exponential number of users who purchase online products and express their opinions in form of reviews. It is observed that recommending the deserving products to people often depend on the sentiment expressed in the reviews. Classification of the reviews as genuine and fake is one of the hardest problems in the current world. Feature Engineering is an active area of research in text analytics and opinion mining and plays a significant role in extracting features from reviews. Genuine reviews often contain higher percentage of concrete information or domain specific information as these reviews are written based on experience, but spam reviews often lack this information This paper focuses on, using Latent Dirichlet Allocation topic model to extract domain features in product reviews and use this as one of the main features to identify fake reviews.

Spam Review Detection Techniques: A Systematic Literature Review

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

Spam review detection using spiral cuckoo search clustering method

Nowadays, online reviews play an important role in customer’s decision. Starting from buying a shirt from an e-commerce site to dining in a restaurant, online reviews has become a basis of selection. However, peoples are always in a hustle and bustle since they don’t have time to pay attention to the intrinsic details of products and services, thus the dependency on online reviews have been hiked. Due to reliance on online reviews, some people and organizations pompously generate spam reviews in order to promote or demote the reputation of a person/product/organization. Thus, it is impossible to identify whether a review is a spam or a ham by the naked eye and it is also impractical to classify all the reviews manually. Therefore, a spiral cuckoo search based clustering method has been introduced to discover spam reviews. The proposed method uses the strength of cuckoo search and Fermat spiral to resolve the convergence issue of cuckoo search method. The efficiency of the proposed method has been tested on four spam datasets and one Twitter spammer dataset. To validate the efficacy of proposed clustering method it is compared with six metaheuristics clustering methods namely; particle swarm optimization, differential evolution, genetic algorithm, cuckoo search, K-means, and improved cuckoo search. The experimental results and statistical analysis validate that the proposed method outruns the existing methods.

Detecting fake reviews via dynamic multimode network

Online product reviews can greatly affect the consumer's shopping decision. Thus, a larger number of unscrupulous merchants post fake or unfair reviews to mislead consumers for their profit and fame. The common approaches to find these spam reviews are analysing the text similarity or rating pattern. With these common approaches we can easily identify ordinary spammers, but we cannot find the unusual ones who manipulate their behaviour to act just like genuine reviewers. In this paper, we propose a novel method to recognise these unusual ones by using relations among reviewers, reviews, commodities and stores. Firstly, we present four fundamental concepts, which are the quality of the merchandise, the honesty of the review, the trustworthiness of the reviewer and the reliability of the store, thus enabling us to identify the spam reviewers more efficiently. Secondly, we propose our multimode network model for identifying suspicious reviews and then give three corresponding algorithms. Eventually, we find that the multi-view spam detection based on the multimode network can detect more subtle false reviews according to our experiments.

Detecting fake reviews via dynamic multimode network

Online product reviews can greatly affect the consumer's shopping decision. Thus, a larger number of unscrupulous merchants post fake or unfair reviews to mislead consumers for their profit and fame. The common approaches to find these spam reviews are analysing the text similarity or rating pattern. With these common approaches we can easily identify ordinary spammers, but we cannot find the unusual ones who manipulate their behaviour to act just like genuine reviewers. In this paper, we propose a novel method to recognise these unusual ones by using relations among reviewers, reviews, commodities and stores. Firstly, we present four fundamental concepts, which are the quality of the merchandise, the honesty of the review, the trustworthiness of the reviewer and the reliability of the store, thus enabling us to identify the spam reviewers more efficiently. Secondly, we propose our multimode network model for identifying suspicious reviews and then give three corresponding algorithms. Eventually, we find that the multi-view spam detection based on the multimode network can detect more subtle false reviews according to our experiments.

HPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews.

Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created large-scale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.