Opinion Spam Research Articles

A deep feature interaction and fusion model for fake review detection: Advocating heterogeneous graph convolutional network

In contemporary real-world scenarios, opinion spammers are hired to fabricate reviews that unfairly promote or demote particular products or services for personal gain. Although considerable attention has been devoted to addressing the problem, existing approaches often overlook the heterogeneous nature of reviewer–product interactions. Specifically, the correlation between review text (comments) and overall ratings, which provides various latent rich information to expose fake reviews, remains inadequately explored. Current methodologies focus on limited interactions, such as reviewer–review, product–review, or reviewer–product interactions, while neglecting significant aspects like reviewer–review–product and reviewer–rating–product interactions, leading to inadequate classifier performance. Motivated by this observation, this study proposes a novel Deep Feature Interaction and Fusion Model (DFIFM) whose ideas are five-folds: (a) constructing a reviewer–product interaction bipartite graph that represents heterogeneous feature node interactions through review text and overall rating values; (b) recognizing the existing mutual interactive relationship between review text and overall rating features, we construct a unified GCN to gain additional insights into feature relationships and capture mutual heterogeneous interactions between nodes; (c) to handle the encoding of unstructured review text features as edge attributes, we adopt a convolutional neural network (CNN); (d) attention mechanisms and fusion techniques are employed to capture interdependencies among reviewer–product latent features; and (e) a Multilayer Perceptron (MLP) utilizes the resulting latent feature representation for review classification. Experimental results on three publicly available datasets demonstrate its superiority over state-of-the-art baselines.

A Weighted Stacking Ensemble Model With Sampling for Fake Reviews Detection

Customers use reviews as a primary source of information to judge a product or service. Positive reviews help boost companies’ reputations, increasing their revenue by attracting new clients, and increasing the purchasing order size. On the other hand, negative reviews significantly reduce sales, which might be the case due to competitive advantage. Organizations can use fake (i.e., misleading or fraudulent) reviews to generate fast profits by deceiving customers into buying their products. Recently, various methods to assess the legitimacy of reviews have been introduced using advances in machine learning. However, existing methods fall short of achieving highly accurate detection results for unbalanced classes. We aimed to create a spam review identification model using ensemble-based learning while balancing classes using sampling techniques. This article proposes a weighted stacking ensemble model with sampling (WSEM-S) for efficient fake reviews detection. We used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram models to effectively model language data for feature retrieval. The experimental results on three customer reviews datasets: YELPNYC, Deceptive Opinion Spam Corpus (DOSC) v1.4, and Deception datasets show that the proposed model outperforms the conventional machine learning techniques Naïve Bayes, logistic regression, K-nearest neighbor (KNN), random forest, extreme gradient boosting (XGBoost), and convolutional neural network (CNN) as well as the state-of-the-art ensemble models.

Meta heuristic approaches for sentiment analysis

AbstractOn the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews.

A multiview clustering framework for detecting deceptive reviews

Online reviews, which play a key role in the ecosystem of nowadays business, have been the primary source of consumer opinions. Due to their importance, professional review writing services are employed for paid reviews and even being exploited to conduct opinion spam. Posting deceptive reviews could mislead customers, yield significant benefits or losses to service vendors, and erode confidence in the entire online purchasing ecosystem. In this paper, we ferret out deceptive reviews originated from professional review writing services. We do so even when reviewers leverage a number of pseudonymous identities to avoid the detection. To unveil the pseudonymous identities associated with deceptive reviewers, we leverage the multiview clustering method. This enables us to characterize the writing style of reviewers (deceptive vs normal) and cluster the reviewers based on their writing style. Furthermore, we explore different neural network models to model the writing style of deceptive reviews. We select the best performing neural network to generate the representation of reviews. We validate the effectiveness of the multiview clustering framework using real-world Amazon review data under different experimental scenarios. Our results show that our approach outperforms previous research. We further demonstrate its superiority through a large-scale case study based on publicly available Amazon datasets.

Understanding Large-Scale Network Effects in Detecting Review Spammers

Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed thatSpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.

A Contextual Relationship Model for Deceptive Opinion Spam Detection.

The promotion of e-commerce platforms has changed the lifestyle of several people from traditional marketing to digital marketing where businesses are made online and the concurrence reached high levels. These platforms have helped the ease of purchases while providing more advantages to the customers such as benefiting from a wide range of high-quality products, low prices, buying at any time, and more importantly supplying information and reviews about the products, and so on. Unfortunately, a plethora of companies mislead the customers to buy their products or demote the competitors' by using deceptive opinion spams which has a negative impact on the decision and the behavior of the purchasers. Deceptive opinion spams are written deliberately to seem legitimate and authentic so that to misguide or delude the customer's purchases. Consequently, the detection of these opinions is a hard task due to their nature for both humans and machines. Most of the studies are based on traditional machine learning and sparse feature engineering. However, these models do not capture the semantic aspect of reviews. According to many researchers, it is the key to the detection of deceptive opinion spam. Besides, only a few studies consider using contextual information by adopting neural networks in comparison with plenty of traditional machine learning classifiers. These models face numerous shortcomings as long as their representations are obtained while mining each review considering only words, sentences, reviews, or a combination of them, thereby classifying them based on their representations. In fact, deceptive opinions are written by the same deceivers belonging to the same companies with similar aims to promote or demolish a product. In other words, Deceptive opinion spams tend to be semantically coherent with each other. To the best of our knowledge, no model tries to obtain a representation based on the contextual relationships between opinions. This article proposes to use a capsule neural network, bidirectional long short-term memory, attention mechanism, and paragraph vector distributed bag of words to detect deceptive opinion spam. Our model provides a powerful representation of the opinions since it centers on the preservation of their contexts and the relationships between them. The results show that our model significantly outperforms the existing state-of-the-art models.

Detecting Spam Reviews in Arabic by Deep Learning

Online reviews are frequently used by consumers to make decisions about online purchases, hotel bookings, car rentals, and other choices because online shopping has grown in popularity over the past few years. Reviews are now crucial to both the customer and the business. As writing fake reviews comes with financial gain, opinion spam activities have increased. Some unethical companies may hire workers to write reviews to influence consumers’ purchasing decisions; therefore, detecting spam reviews is a very important task. We compiled a large dataset of Arabic reviews consisting of spam and non-spam that are categorized by crowd-sourcing approach. Then, we applied deep learning algorithms to detect spam reviews. To the best of our knowledge, there are no prior studies utilized deep learning to classify reviews that are written in Arabic. Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models were used, and an accuracy of 97% was achieved by both algorithms. To further improve the results, unbalanced issues were solved by oversampling and undersampling techniques. The results of them are improvements in the precision, recall, and F1-score for spam reviews. For example, in CNN F1-score for spam class increased from 79% to 90% with undersampling and became 82% with oversampling.

Automatic identification of false opinions in social networks

This paper presents the problem of detecting false opinions in social networks, also called "opinion spam", describing how lies can be automatically detected using different methods. It has been shown that deception is frequently present in everyday communication in social networks. Deception detection is a well-known challenging problem in any research area, basically because the human ability to detect deception is deficient. Particular studies on social psychology and communications show that the accuracy rates of people's abilities to detect deception are in the range of 55 to 58%, i.e., slightly better than chance. This paper addresses the specific problem of deception detection in communication. Emphasis is placed on those approaches that use affective resources such as categorical and psychometric information provided by natural language processing tools. Finally, we focus on the identification of opinion spam, whose detection is very important for reliable opinion mining. Results obtained using different machine learning methods are presented. The results obtained allow us to see the feasibility of the proposed methodology to carry out the detection of false opinions in social networks by obtaining accuracy values higher than 80%.

Deceptive opinion spam detection using feature reduction techniques

Deceptive opinion spam detection using feature reduction techniques

DETECTING DECEPTIVE REVIEWS: AN INTEGRATED MACHINE LEARNING APPROACH

In recent years, online reviews have become a crucial factor in promoting products and services. However, the rise of fake reviews has posed a significant challenge. Businesses, marketers, and advertisers often resort to embedding fake reviews to attract customers or undermine their competitors. Deceptive reviews have become a common practice, as they serve as a means of promoting one''''s own business or tarnishing the reputation of rivals. Consequently, the identification of deceptive reviews has emerged as a critical and ongoing research area. This research paper presents a machine learning model approach to detect deceptive reviews. The study focuses on experiments conducted using a deceptive opinion spam corpus dataset, specifically targeting restaurant reviews. An n-gram model combined with max features is developed to identify deceptive content, with a particular emphasis on fake reviews. Additionally, a benchmark study is conducted to explore the performance of two different feature extraction techniques and their application in five machine learning classification techniques. The experimental findings demonstrate that the passive aggressive classifier outperforms other algorithms, achieving the highest accuracy not only in text classification but also in identifying fake reviews. Moreover, the research delves into the identification of deceptive reviews and explores diverse feature extraction and machine learning techniques to improve the model''''s accuracy.

Detecting Product Review Spammers Using Principles of Big Data

The growing consumerism has led to the importance of online reviews on the Internet. Opinions voiced by these reviews are taken into consideration by many consumers for making financial decisions online. This has led to the development of opinion spamming for profitable motives or otherwise. This work has been done to tackle the challenge of identifying such spammers, but the scale of the real-world review systems demands this problem to be tackled as a big data challenge. So, an effort has been made to detect online review spammers using the principle of big data. In this article, a rating-based model has been studied under the light of large-scale datasets (more than 80 million reviews by 20&#x00A0;million reviewers) using the Hadoop and Spark frameworks. Scale effects have been identified and mitigated to provide better context to large review systems. An improved computational framework has been presented to compute the overall spamcity of reviewers using exponential smoothing. The value of the smoothing factor was set empirically. Finally, future directions have been discussed.

Mining Weak Relations Between Reviews for Opinion Spam Detection

Online reviews play a significant role in purchase decisions of consumers by providing feedback information from buyers of products. In order to mislead consumers, opinion spammers are hired to write fake reviews to promote or demote specific products for illegitimate benefits. Existing methods for spam review detection mainly focused on designing manual features, which highly rely on expert knowledge. Although recent works utilized deep learning methods to automatically learn the semantics of reviews through the inherent user-review-product strong relation, they fail to capture the weak relations between reviews at the content, sentiment and temporal levels, which provides various semantic information to expose fake reviews. Moreover, the imbalanced class distribution in spam detection issues makes this work even more challenging. To address the above problems, we propose a novel Weak-Strong Unified Network (WSUN) for opinion spam detection. Multi-level weak relation graphs are constructed to reveal the abnormal behavioral patterns of spammers, which aggregates the semantics of strong relations by graph convolutional networks and extracts comprehensive review representation by utilizing relation-level attention mechanism. In addition, a graph-based over-sampling method is devised to mitigate the impact of imbalanced class distribution. Extensive experimental results on real-world datasets show that our model is more effective than the state-of-the-art methods.

Prevention of Fake Comments using web3

The volume of information on the internet is currently rising dramatically. Social media platforms/e-commerce market place is producing a lot of data, including reviews, comments, and opinions, every day. As there are a number of fake reviews should incorporate Spam detection to produce a genuine opinion. Fake reviews are growing problem in online shopping, and they have a significant impact on consumer’s decision-making. Many people today base their decisions when choosing a product or service on social media opinions. Because so many false or phoney evaluations have been written by businesses or individuals for a variety of reasons, detecting opinion spam is a difficult and time-consuming task. They produce fictitious reviews to deceive users or automated detection systems by elevating or degrading the reputations of their target products in order to elevate or lower them. In this article, we’ll regulate it by leveraging blockchain technology to make the review system more authentic by allowing only legitimate product purchasers to submit evaluations using their account credentials. We use the Ethereum blockchain to authenticate user credentials, and we only permit verified users to purchase things. Also, we only permit customers to leave reviews or comments on products, ensuring that the reviews are accurate.

Detection of Fake Reviews Using Machine Learning Techniques

Online marketing generates vast amounts of digital information, which is then used to advertise millions of goods and services. Finding the best services or goods that meet the need is, therefore, difficult. Customers make decisions based on evaluations or opinions expressed by third parties. In this cutthroat environment. For the past few years, some businesses have employed writers to fabricate favorable reviews of their services or goods or unfairly critical reviews of those of their rivals in order to increase the number of false reviews. Review sites are increasingly having to deal with the distribution of false information that either benefits or hurts particular firms. Different types of opinion spam are used to deceive both human readers and automated sentiment analysis and opinion mining systems. Different strategies have been put forth as a result, allowing for the evaluation of the user-generated content's credibility. The current paper provides a quick introduction to the examination of opinion mining and the review-centric attributes that have been suggested for use in machine learning techniques that use graphical methods to identify bogus reviews. Along with a review of the related literature and a discussion of it.

An Effective Framework for design of Dataset Using Twitter

The rapid expansion of internet usage and related services like social media and blogs has increased people's level of expressiveness in day-to-day life. Social media platforms like Twitter and Facebook facilitate people to interact and exchange opinions about people, products, and services. As a result, a vast amount of data is available online in the form of views, tweets, messages, audio, and videos. An interface is needed to collect knowledge and insights from the various tweets, ideas, and comments. Thus we have proposed the Twitter API-based Interface, able to perform Hashtag searches and extract tweets from Twitter along with the ample number of fields related to the Twitter object. Using the interface, the 55 properties of each tweet are collected and used for further investigations. The python-based library called Tweepy is used to interact with the Twitter API. Due to the availability of real-worlddata, various issues related to text analysis can be addressed. The problems such as Sentiment Analysis, Opinion Mining, Implicit and Explicit detection, genuineness of views, and Opinion Spam detection can be addressed using the dataset availability.

A comprehensive survey of various methods in opinion spam detection

A comprehensive survey of various methods in opinion spam detection

Deceptive reviews and sentiment polarity: Effective link by exploiting BERT

Today, reviews are the advertising medium par excellence through which companies are able to influence customers’ spending decisions. Although the initial purpose of reviews was to provide companies with a feedback tool to improve products and services based on customer needs, they soon became a way to climb the sales rankings, often illegally. In fact, deceptive and fake reviews have managed to evade the often non-existent means of validation of online platforms, proliferating a new business. To combat this phenomenon, several classification methods have been developed to train automated tools in the arduous task of distinguishing between genuine and misleading reviews, the most recent based on machine and deep learning techniques. This paper proposes a multi-label classification methodology based on the Google BERT neural language model to build a deceptive review detector aided by its sentiment awareness: improved modeling of the link between sentiment polarity and deceptiveness during the fine-tuning phase by exploiting the Binary Cross Entropy with Logits loss function adds to the advantages provided by pre-trained contextual models, which are able to capture word polysemy through word embeddings and benefit from pre-training on huge corpora. Tests were performed on the Deceptive Opinion Spam Corpus and Yelp New York City datasets, providing a quantitative and qualitative analysis of the results which, when compared with the state of the art available in the literature, showed an encouraging increase in performance.

Vec4Cred: a model for health misinformation detection in web pages

Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.

A text classification method based on a convolutional and bidirectional long short-term memory model

Text classification refers to labelling text with specified labels, and it is widely used in public opinion supervision, spam detection, and other fields. However, due to the complex semantics of natural language and the difficulty of extracting semantic features, users of traditional methods encounter difficulties when trying to achieve better classification results. In response to this problem, a text classification method based on the CBM (Convolutional and Bi-LSTM Model) model, which can extract shallow local semantic features and deep global semantic features, is proposed. First, the text is vectorised using the Glove model in the embedding layer. Then, the vector text is sent to the Multiscale Convolutional Neural Network (MCNN) and the Bidirectional Long Short-Term Memory network (Bi-LSTM) respectively. The Bi-LSTM layer is also designed in the present work with use of mixed attention to extract deeper semantic features. Finally, the MCNN features and Bi-LSTM features are fused and sent to the softmax layer for classification. Experimental results show that the model can significantly improve the accuracy of text classification.

Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms

E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is hard to identify opinion spammers automatically, including the absence of reliable labeled data. This limitation precludes an off-the-shelf application of a machine learning pipeline. We propose a new method for classifying reviewers as spammers or benign, combining machine learning with a message-passing algorithm that capitalizes on the users' graph structure to compensate for the possible scarcity of labeled data. We devise a new way of sampling the labels for the training step (active learning), replacing the typical uniform sampling. Experiments on three large real-world datasets from Yelp.com show that our method outperforms state-of-the-art active learning approaches and also machine learning methods that use a much larger set of labeled data for training.