Opinion Spam Detection Research Articles

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.

Deceptive opinion spam detection using feature reduction techniques

Deceptive opinion spam detection using feature reduction techniques

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.

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

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.

Deceptive opinion spam detection approaches: a literature survey

Deceptive opinion spam detection approaches: a literature survey

A Study on Diverse Methods and Performance Measures in Sentiment Analysis

: With the vast development of internet technology 2.0, millions of people are sharing their opinions on different social networking sites. To obtain the necessary information from the huge volume of user-generated data, the attention on sentiment analysis among the research community is growing. Growth and prominence of sentiment analysis is synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps to produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of user-generated content such as noisy, irrelevant information and fake reviews, there is a significant demand for an effective methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies and highlights the challenges and performance factors of various approaches of sentiment analysis including text preprocessing, opinion spam detection, and aspect level sentiment analysis. Background: User-generated content is growing all over the globe and people more eagerly express their views on social media towards various aspects. The opinionated text is difficult to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the usergenerated data. Objective: The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis namely text pre-processing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.

Temporal Opinion Spam Detection by Multivariate Indicative Signals

Online consumer reviews reflect the testimonials of real people, unlike e.g., ads. As such, they have critical impact on potential consumers, and indirectly on businesses. Problematically, such financial incentives have created a market for spammers to fabricate reviews to unjustly promote or demote businesses, activities known as opinion spam (Jindal and Liu 2008). Most existing work on this problem have formulations based on static review data, with respective techniques operating in an offline fashion. Spam campaigns, however, are intended to make most impact during their course. Abnormal events triggered by spammers’ activities could be masked in the load of future events, which static analysis would fail to identify. In this work, we approach the opinion spam problem with a temporal formulation. Specifically, we monitor a list of carefully selected indicative signals of opinion spam over time and design efficient techniques to both detect and characterize abnormal events in real-time. Experiments on two different datasets show that our approach is fast, effective, and practical to be deployed in real-world systems.

Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns

Although opinion spam (or fake review) detection has attracted significant research attention in recent years, the problem is far from solved. One key reason is that there is no large-scale ground truth labeled dataset available for model building. Some review hosting sites such as Yelp.com and Dianping.com have built fake review filtering systems to ensure the quality of their reviews, but their algorithms are trade secrets. Working with Dianping, we present the first large-scale analysis of restaurant reviews filtered by Dianping's fake review filtering system. Along with the analysis, we also propose some novel temporal and spatial features for supervised opinion spam detection. Our results show that these features significantly outperform existing state-of-art features.

Opinion Mining On Web-Based Communities Using Optimised Clustering Algorithms

People like to consume content by downloading it from the internet, together with contribution and creation of new content, thanks to the growth of social networks and the Web. People also turn out to be more anxious to communicate and impart their insights on the web with respect to everyday exercises or worldwide issues. Sentiment analysis and opinion mining are rapidly evolving as a result of the rapid growth of web-based platforms such as Facebook, Reddit, Twitter, and others. This is from the world of data mining and NLP, especially in the mining of web data and the mining of texts. Why has sentiment analysis been more popular in recent years, which is also known as opinion mining? Whenever we attempt to choose to buy an item, we are probably going to hear the thoughts of companions or family members and do a few overviews before we buy the item. Opinions are now undoubtedly the main drivers of our behavior. The positive, negative, or neutral polarities are consistently seen in the opinions. Opinion mining can be used to discover opinion polarity classification, subjectivity identification, spam detection, and opinion summarization using emotional classification.

SC-Com: Spotting Collusive Community in Opinion Spam Detection

In many cases, our decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers’ opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, we can spot the possibility to detect them from their collusiveness. In this paper, we propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, we extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. We show that our method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, our approach showed prominent performance while only considering primary data such as time and ratings.

Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification

Opinion spam detection is concerned with identifying fake reviews that are deliberately placed to either promote or discredit a product. Opinionated social media like product reviews are increasingly important resources for people as well as businesses in the decision-making process and can be easily manipulated by opportunistic individuals. To reduce this increasing impact of opinion spams, opinion spam detection approaches have been proposed, which adopt mostly supervised classification methods. However, in practice, the provided data is largely not labeled and therefore semi-supervised learning approaches are required instead. To this end, this study aims to analyze the effectiveness of several semi-supervised learning approaches for opinion spam classification. Four different semi-supervised methods are evaluated on a dataset of both genuine and deceptive hotel reviews. The results are compared with several traditional classification methods using the same amount of labeled data. According to this study, the self-training algorithm with Naive Bayes as the base classifier yields 93% accuracy. Results show that self-training is the only approach, out of the four tested semi-supervised models, that outperforms traditional supervised classification models when limited data is available. This study further shows that self-training can mitigate labeling efforts while retaining high model performance, which is useful for scenarios where limited data is available or retrieving labeled data is more costly.

An unsupervised approach to detect review spam using duplicates of images, videos and Chinese texts

Intuitively, image- or video-based recommendations seem to be more reliable than those containing plain text, and these types of recommendations have recently become widely encouraged and commonly seen across opinion sharing platforms. Considering their potential for manipulation, graphs (e.g., images and videos) are more vulnerable to spam than scripts. However, most state-of-the-art solutions for opinion spam detection are exclusively devoted to natural language parsing, and less work has been done concerning photos or videos. After investigating the top two business-to-customer websites, i.e., JD.com and TMALL.com, we propose an unsupervised approach to label suspected spam based on different types of duplication across images, videos and Chinese texts. Experiments verified the effectiveness of this approach and obtained several conclusions: 1) the situation of image spam is more severe than that of video and text spam; 2) for manipulation, borrowing something from a marketing page is less attractive than stealing from other reviewers; 3) in addition to using identical texts, spammers also use fictitious rare incidents to influence customers; and 4) overlapping duplications of images, videos and texts are common.

A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM

Nowadays, e-commerce has become a part of our daily life in such a way that people's decision for buying products or choosing services highly depends on comments, reviews, and rates, which are posted on related businesses’ website and other social media. Because of the importance and prevalence of these sources of information, fraudsters are tempted to use fraudulently opinion sharing platforms in order to promote or to discredit some target products or services. Although a wide range of approaches have been proposed to address this problem and to help distinguish between the deceptive or fraudulent opinions from the trustful ones, this is still a challenging problem. Lack of well-defined deceptive data samples or insufficiency of spam review instances in the training sets cause supervised techniques facing an imbalanced classes problem. Furthermore, even in the golden normal opinion datasets, there is a possibility of the presence of some abnormal records or outliers. To deal with these two issues, we propose a robust and non-convex semi-supervised algorithm called “Ramp One-Class SVM”. In the proposed method, one-class SVM is adopted to handle the lack of labeled data for the deceptive opinions and by taking the advantages of non-convex properties of the Ramp loss function, we eliminate the effects of outliers and non-review opinions. The performance of the proposed method is evaluated by an artificial dataset and two real datasets including Ott and Yelp crowdsourced datasets. The results show the superiority of our method by achieving an accuracy of 92.13% and 74.37% for Ott and Yelp crowdsourced datasets, respectively. The obtained results also reveal the effectiveness of the proposed model in terms of precision, recall, generalization power, and robustness to the outliers.

Online Fraud Review Detection Using Data Mining

Online reviews have great impact on today’s business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Nowadays, there are a number of people using social media opinions to create their call on shopping for product or service. Opinion Spam detection is an exhausting and hard problem as there are many faux or fake reviews that have been created by organizations or by the people for various purposes. They write fake reviews to mislead readers or automated detection system by promoting or demoting target products to promote them or to degrade their reputations, opportunistic individuals or groups try to manipulate product reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.

An anomaly detection framework for time-evolving attributed networks

Real-world information systems are naturally dynamic, and they are usually represented as a time-evolving attributed network (i.e., a sequence of static attributed networks). Recently, there is a surge of research interests in finding anomalous nodes upon attributed networks due to its significant implications in many high-impact applications, such as financial fraud detection, network intrusion detection, and opinion spam detection, to name a few. Despite the importance of anomaly detection in time-evolving attributed network, a vast majority of existing methods fail to capture the evolution of the underlying networks properly, as they regard the whole system as static and neglect the evolution process. Meanwhile, they treat all the attributes and the instances equally, ignoring the existence of noisy which may lead to the adverse effects to the detection results. To tackle these problems, in this paper, we propose a novel dynamic anomaly detection framework on time-evolving attributed networks on the basis of residual analysis, namely AMAD. Under the assumption of temporal smoothness property, AMAD leverages the small smooth disturbance between two consecutive time stamps to characterize the evolution of networks for incrementally update. Experiments conducted on both synthetic and real-world time-evolving attributed networks show the superiority of our proposed method in detecting anomalies. Moreover, our method is competitive in terms of efficiency compared to the existing work.

Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

Abstract Purpose This paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection. Design/methodology/approach Based on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection. Findings Our findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual. Research limitations The analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com. Practical implications The results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information. Originality/value To the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.