Review Spam Detection Research Articles

An Ensemble Framework for Spam Detection on Social Media Platforms

As various review sites grow in popularity and begin to hold more sway in consumer preferences, spam detection has become a burgeoning field of research. While there have been various attempts to resolve the issue of spam on the open web, specifically as it relates to reviews, there does not yet exist an adaptive and robust framework out there today. We attempt to address this issue in a domain-specific manner, choosing to apply it to Yelp.com first. We believe that while certain processes do exist to filter out spam reviews for Yelp, we have a comprehensive framework that can be extended to other applications of spam detection as well. Furthermore, our framework exhibited a robust performance even when trained on small datasets, providing an approach for practitioners to conduct spam detection when the available data is inadequate. To the best of our knowledge, our framework uses the most number of extracted features and models in order to finely tune our results. In this paper, we will show how various sets of online review features add value to the final performance of our proposed framework, as well as how different machine learning models perform regarding detecting spam reviews.

Ensemble Classifiers Employed for Spam Review Detection

The advancement of technology and the use of internet have changed many aspects of human culture over the years. Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision. Here the web or user-generated content from consumers of such products and services, known as reviews, are exploited by spam reviewers to falsely promote or downgrade some targeted products. Despite potential solutions, Identifying and preventing review spam are still one of the top challenges faced by web search engines today. Therefore, in the quest to provide a more improved and efficient classification of review spam, this research probed different techniques in order to find most effective solution to spam detection. The research employed three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier. The Arching classifier performs the weighted voting that produces the final class label with performance and accuracy higher than either of the individual base classifiers. Cross-validation is used as evaluation metrics to measure the performance or effectiveness of the ensemble classifiers while the experimental results shows that the ensemble classifiers achieve the best results compared to the single based classifier in terms of Precision, Recall, F1-measure and Accuracy.

Review Manipulation: Literature Review, and Future Research Agenda

Abstract Background: The phenomenon of review manipulation and fake reviews has gained Information Systems (IS) scholars’ attention during recent years. Scholarly research in this domain has delved into the causes and consequences of review manipulation. However, we find that the findings are diverse, and the studies do not portray a systematic approach. This study synthesizes the findings from a multidisciplinary perspective and presents an integrated framework to understand the mechanism of review manipulation. Method: The study reviews 88 relevant articles on review manipulation spanning a decade and a half. We adopted an iterative coding approach to synthesizing the literature on concepts and categorized them independently into potential themes. Results: We present an integrated framework that shows the linkages between the different themes, namely, the prevalence of manipulation, impact of manipulation, conditions and choice for manipulation decision, characteristics of fake reviews, models for detecting spam reviews, and strategies to deal with manipulation. We also present the characteristics of review manipulation and cover both operational and conceptual issues associated with the research on this topic. Conclusions: Insights from the study will guide future research on review manipulation and fake reviews. The study presents a holistic view of the phenomenon of review manipulation. It informs various online platforms to address fake reviews towards building a healthy and sustainable environment.

Detecting Spam Product Reviews in Roman Urdu Script

ReferencesIn recent years, online customer reviews have become the main source to determine public opinion about offered products and services. Therefore, manufacturers and sellers are extremely concerned with customer reviews, as these can have a direct impact on their businesses. Unfortunately, there is an increasing trend to write spam reviews to promote or demote targeted products or services. This practice, known as review spamming, has posed many questions regarding the authenticity and dependability of customers’ review-based business processes. Although the spam review detection (SRD) problem has gained much attention from researchers, existing studies on SRD have mostly worked on datasets of English, Chinese, Arabic, Persian, and Malay languages. Therefore, the objective of this research is to identify the spam in Roman Urdu reviews using different classification models based on linguistic features and behavioral features. The performance of each classifier is evaluated in a number of perspectives: (i) linguistic features are used to calculate accuracy (F1 score) of each classifier; (ii) behavioral features combined with distributional and non-distributional aspects are used to evaluate accuracy (F1 score) of each classifier; and (iii) the combination of both linguistic and behavioral features (distributional and non-distributional aspects) are used to evaluate the accuracy of each classifier. The experimental evaluations demonstrated an improved accuracy (F1 score: 0.96), which is the result of combinations of linguistic features and behavioral features with the distributional aspect of reviewers. Moreover, behavioral features using distributional characteristic achieve an accuracy (F1 score: 0.86) and linguistic features shows accuracy (F1 score: 0.69). The outcome of this research can be used to increase customers’ confidence in the South Asian region. It can also help to reduce spam reviews in the South Asian region, particularly in Pakistan.

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

A Survey on Feature Selection Based Spam Review Detection using Deep Learning Techniques

In recent times, online shoppers are technically knowledgeable and open to product reviews. They usually read the buyer reviews and ratings before purchasing any product from ecommerce website. For the better understanding of products or services, reviews provided by the customers gives the vital source of information. In order to buy the right products for the individuals and to make the business decisions for the Organization online reviews are very important. These reviews or opinions in turn, allow us to find out the strength and weakness of the products. Spam reviews are written in order to falsely promote or demote a few target products or services. Also, detecting the spam reviews has also become more critical issue for the customer to make good decision during the purchase of the product. A major problem in identifying the fake review detection is high dimensionality of the feature space. Therefore, feature selection is an essential step in the fake review detection to reduce dimensionality of the feature space and to improve the classification accuracy. Hence it is important to detect the spam reviews but the major issues in spam review detection are the high dimensionality of feature space which contains redundant, noisy and irrelevant features. To resolve this, Deep Learning Techniques for selecting features is necessary. To classify the features, classifiers such as Naive Bayes, K Nearest Neighbor are used. An analysis of the various techniques employed to identify false and genuine reviews has been surveyed.

Spam review detection using LSTM autoencoder: an unsupervised approach

The review of online products or services is becoming a major factor in the user’s purchasing decisions. The popularity and influence of online reviews attract spammers who intend to elevate their products or services by writing positive reviews for them and lowering the business of others by writing negative reviews. Traditionally, the spam review identification task is seen as a two-class classification problem. The classification approach requires a labelled dataset to train a model for the environment it is working on. The unavailability of the labelled dataset is a major limitation in the classification approach. To overcome the problem of the labelled dataset, we propose an unsupervised learning model combining long short-term memory (LSTM) networks and autoencoder (LSTM-autoencoder) to distinguish spam reviews from other real reviews. The said model is trained to learn the patterns of real review from the review’s textual details without any label. The experimental results show that our model is able to separate the real and spam review with good accuracy.

LDA_RAD: A Spam review detection method based on topic model and reviewer anomaly degree

Online shopping has become a popular activity among consumers. Reviews have become important standards for consumers to buy things. To detect the spam re-views that do not correctly response the right information of goods, a method based on topic model and reviewer anomaly degree is raised. This method divides spam reviews into content-type and deceptive spam review respectively. Firstly, the experimental data set is modelled by LDA topic, which detects the content-type spam reviews with different themes. Then the deceptive spam reviews are detected by the reviewer’s abnormality degree index. It assigns a score to each review according to extracted features and related weights. A score is finally combined with an adaptive weight calculation based on the abnormal period and the reviewer’s similarity to obtain the review score. The review with a high score is considered to be a spam review, and the review with a low score is a true review. Experiments show that this method has a certain improvement on the recognition rate of spam reviews.

An Attention-Based Unsupervised Adversarial Model for Movie Review Spam Detection

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved. Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews. Most existing research manually extracted features and labeled training samples, which are usually complicated and time-consuming. This paper focuses primarily on a neglected emerging domain - movie review, and develops a novel unsupervised spam detection model with an attention mechanism. By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews. An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users' review style for different genres of movies. The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies. The experimental results demonstrate the superior performance of the proposed approach.

Generating behavior features for cold-start spam review detection with adversarial learning

Due to the wide applications, spam detection has long been a hot research topic in both academia and industry. Existing studies show that behavior features are effective in distinguishing the spam and legitimate reviews. However, it usually takes a long time to collect such features and thus is hard to apply them to cold-start spam review detection tasks. Recent advances leveraged the neural network to encode the various types of textual, behavior, and attribute information for this task. However, the inherent problem, i.e., lack of effective behavior features for new users who post just one review, is still unsolved.In this paper, we exploit the generative adversarial network (GAN) for addressing this problem. The key idea is to generate synthetic behavior features (SBFs) for new users from their easily accessible features (EAFs). Specifically, we first select six well recognized real behavior features (RBFs) existing for regular users. We then train a GAN framework including a generator to generate SBFs from their EAFs including text, rating, and attribute features, and a discriminator to discriminate RBFs and SBFs. We design a new implementation of generator and discriminator for effective training. The trained GAN is finally applied to new users for generating synthetic behavior features. We conduct extensive experiments on two Yelp datasets. Experimental results demonstrate that our proposed framework significantly outperforms the state-of-the-art methods.

Spam Detector: A Solution for Finding Bogus Opinions and Spammer Classification in E-Commerce

In this paper we propose a system called Spam Detector to find out spam reviews of particular product. This framework uses spam features which helps to review datasets. In proposed system four different categories of features are included such as review behavioral (based on behaviour of review) , user behavioral (based on behaviour of user), review linguistic (based on linguistic pattern of review) and user linguistic (based on linguistic pattern of user). In today’s world, most of people take decision based on information available through social media reviews on a topic or product. Due to this it is possible that anyone can post a review for a particular product which give a chance for spammers to post junk reviews related to particular product and facilities. To find out such spammers and the spam review is becoming important. Some methods exist to cope up with this problem, but these methods hardly detect spam reviews, and are not based on the extracted feature type. Weight of spam features are used to gain better results in the form of different aspects tested on real world review datasets. Here the used dataset is from Amazon websites. The experimental results show that Spam Detector beats the present method.

Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

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.

An enhanced feature‐based sentiment analysis approach

AbstractIn the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews.This article is categorized under: Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning

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 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.

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.

Improved Text Mining Techniques for Spam Review Detection

Improved Text Mining Techniques for Spam Review 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.