scholarly journals Opinion Spam Detection based on Annotation Extension and Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 87
Author(s):  
Yuanchao Liu ◽  
Bo Pang
Keyword(s):  
Online Reviews ◽  
Detection Performance ◽  
Spam Detection ◽  
Purchase Decisions ◽  
Novel Approach ◽  
Review Spam ◽  
Opinion Spam ◽  
Improved Performance ◽  
Fake Reviews ◽  
Potential Customers

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.

Survey on Review Spam of E-Commerce Sites

2014 ◽  
Vol 631-632 ◽  
pp. 1190-1193
Author(s):  
Sheng Xiu Yang ◽  
Lu Jie Fan
Keyword(s):  
Online Shopping ◽  
Important Determinant ◽  
Spam Detection ◽  
Product Reviews ◽  
Product Review ◽  
Review Spam ◽  
Quality Of Products ◽  
Fake Reviews ◽  
Potential Customers

Online shopping reviews provide valuable information for customers to compare the quality of products, and many other aspects of future purchases. People increasingly rely on information from E-commerce reviews. Product reviews is an important determinant of potential customers’ buying choices. However, spammers are joining this community to try to mislead consumers by writing fake or unfair reviews to confuse the consumers. Fake product review detection makes an attempt to detect fake reviews and remove them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on review spam. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect review spam are introduced and summarized. The trend of review spam detection is concluded finally.

Opinion Spam Detection in Online Reviews

2017 ◽  
Vol 16 (04) ◽  
pp. 1750036 ◽  
Author(s):  
Ajay Rastogi ◽  
Monica Mehrotra
Keyword(s):  
Personal Experience ◽  
State Of The Art ◽  
Online Reviews ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Sources Of Information ◽  
Online Purchase ◽  
Learning Techniques ◽  
Opinion Spam ◽  
Fake Reviews

Online reviews are the most valuable sources of information about customer opinions and are considered the pillars on which the reputation of an organisation is built. From a customer’s perspective, review information is key to making a proper decision regarding an online purchase. Reviews are generally considered an unbiased opinion of an individual’s personal experience with a product, but the underlying truth about these reviews tells a different story. Spammers exploit these review platforms illegally because of incentives involved in writing fake reviews, thereby trying to gain an advantage over competitors resulting in an explosive growth of opinion spamming. The present study analyses and categorises the available literature on opinion spamming according to three detection targets: (1) opinion spam, (2) opinion spammers, and (3) collusive opinion spammer groups. The study further highlights and divides opinion spamming into three types based on textual and linguistic, behavioural, and relational features. Moreover, several state-of-the-art machine-learning techniques for opinion spam detection have also been discussed in the study. It concludes with a summary of the research articles on opinion spam detection and some interesting results to assist researchers for further exploration of the domain.

Review Spam Detection by Highlighting Potential Spammers and Diminishing Their Effect

2018 ◽  
Vol 14 (1) ◽  
pp. 54-76
Author(s):  
Fatemeh Keshavarz ◽  
Ayeshaa Abdul Waheed ◽  
Btissam Rachdi ◽  
Reda Alhajj
Keyword(s):  
Opinion Mining ◽  
Spam Detection ◽  
Test Results ◽  
The Public ◽  
Group Activities ◽  
Review Spam ◽  
Fake Reviews ◽  
Potential Customers

Nowadays, millions of products and services are available to the public online. Therefore, searching for the best products which meets individuals' expectations would be difficult due to the existence of too many alternative choices. One of the most reliable approaches to choose a product or service is to exploit the experience of people who have already tried them, and are expected to have reported their almost honest opinions about them. A reviewing system is a place where individuals share their experience on products and services. Individuals may read and/or write their reviews which may be neutral and professional or biased. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and may be to watch their competitors. However, the popularity of reviewing systems ignites this motivation for some people to try to influence viewers by entering their fake reviews to promote some products or defame some others. These spam reviews should be detected and eliminated to prevent misleading potential customers and unethically affect the market. Opinion mining should be adapted to locate and eliminate potential spam reviews. In this paper, some review spam detection approaches have been proposed and examined over a sample dataset. The proposed approaches consider patterns that existed in trends of reviews, as well as reviewers' behavior. The approaches depend on various strategies such as observing abnormal trends, detecting uncommon or suspicious behaviors, investigating group activities, among others. The reported test results revealed some promising outcome.

Experience

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Michela Fazzolari ◽  
Francesco Buccafurri ◽  
Gianluca Lax ◽  
Marinella Petrocchi
Keyword(s):  
Relative Frequency ◽  
Service Providers ◽  
Real Data ◽  
Online Reviews ◽  
Purchase Decision ◽  
The Past ◽  
Supervised Classifiers ◽  
Relative Frequency Distribution ◽  
Opinion Spam ◽  
Fake Reviews

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.

scholarly journals Spam Review Detection Techniques: A Systematic Literature Review

2019 ◽  
Vol 9 (5) ◽  
pp. 987 ◽  
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Ghulam Rasool ◽  
Ibrar Hussain ◽  
Mohammad Kaleem
Keyword(s):  
Feature Extraction ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Online Reviews ◽  
Detection Methods ◽  
Spam Detection ◽  
Successful Implementation ◽  
Comprehensive Review ◽  
Detection Techniques ◽  
Review Spam

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.

scholarly journals GANs for Semi-Supervised Opinion Spam Detection

2019 ◽  
Author(s):  
Gray Stanton ◽  
Athirai A. Irissappane
Keyword(s):  
Text Classification ◽  
State Of The Art ◽  
Ground Truth ◽  
Online Reviews ◽  
Spam Detection ◽  
Large Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Opinion Spam ◽  
Source Of Information

Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews, only a few of them have been labeled spam or non-spam. We propose spamGAN, a generative adversarial network which relies on limited labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor data show that spamGAN outperforms existing techniques when labeled data is limited. spamGAN can also generate reviews with reasonable perplexity.

scholarly journals THE DYNAMIC EFFECTS OF ONLINE PRODUCT REVIEWS ON PURCHASE DECISIONS

2018 ◽  
Vol 24 (5) ◽  
pp. 2045-2064 ◽  
Author(s):  
Jia Chen ◽  
Gang Kou ◽  
Yi Peng
Keyword(s):  
Decision Process ◽  
Online Reviews ◽  
Online Review ◽  
Purchase Decision ◽  
Product Reviews ◽  
Purchase Decisions ◽  
Dynamic Effects ◽  
Initial Opinion ◽  
Initial Impressions ◽  
Potential Customers

Previous studies have demonstrated that online reviews play an important role in the purchase decision process. Though the effects of positive and negative reviews to consumers’ purchase decisions have been analyzed, they were examined statically and separately. In reality, online review community allows everyone to express and receive opinions and individuals can reexamine their opinions after receiving messages from others. The goal of this paper is to study how potential customers form their opinions dynamically under the effects of both positive and negative reviews using a numerical simulation. The results show that consumers with different membership levels have different information sensitivities to online reviews. Consumers at low and medium membership levels are often persuaded by online reviews, regardless of their initial opinion about a product. On the other hand, online reviews have less effect on consumers at higher membership levels, who often make purchase decisions based on their initial impressions of a product.

scholarly journals Review Pollution: Pedagogy for a Post-Truth Society

2021 ◽  
Vol 9 (3) ◽  
pp. 144-154
Author(s):  
Emily West
Keyword(s):  
Online Reviews ◽  
Online Information ◽  
Business Practices ◽  
Purchase Decisions ◽  
High Stakes ◽  
Promotional Strategy ◽  
Fertile Ground ◽  
Fake Reviews ◽  
Product Ratings ◽  
New Strategies

Consumer reviews on platforms like Amazon are summarized into star ratings, used to weight search results, and consulted by consumers to guide purchase decisions. They are emblematic of the interactive digital environment that has purportedly transferred power from marketers to ‘regular people,’ and yet they represent the infiltration of promotional concerns into online information, as has occurred in search and social media content. Consumers’ ratings and reviews do promotional work for brands—not just for products but the platforms that host reviews—that money can’t always buy. Gains in power by consumers are quickly met with new strategies of control by companies who depend on reviews for reputational capital. Focusing on ecommerce giant Amazon, this article examines the complexities of online reviews, where individual efforts to provide product feedback and help others make choices become transformed into an information commodity and promotional vehicle. It acknowledges the ambiguous nature of reviews due to the rise of industries and business practices that influence or fake reviews as a promotional strategy. In response are yet other business practices and platform policies aiming to provide better information to consumers, protect the image of platforms that host reviews, and punish ‘bad actors’ in competitive markets. The complexity in the production, regulation, and manipulation of product ratings and reviews illustrates how the high stakes of attention in digital spaces create fertile ground for disinformation, which only emphasizes to users that they inhabit a ‘post-truth’ reality online.

Toward a Theoretical Model of Authentic and Fake User-Generated Online Reviews

2019 ◽  
pp. 104-120
Author(s):  
Snehasish Banerjee ◽  
Alton Y. K. Chua
Keyword(s):  
Theoretical Model ◽  
Online Reviews ◽  
Purchase Decisions ◽  
Theoretical Discussion ◽  
Research Directions ◽  
New Research ◽  
Fake Reviews ◽  
Scholarly Attention

As consumers increasingly rely on user-generated online reviews to make purchase decisions, the prevalence of fake entries camouflaged among authentic ones has become a growing concern. On the scholarly front, this has given rise to two disparate research strands. The first focuses on ways to distinguish between authentic and fake reviews but ignores consumers' perceptions. The second deals with consumers' perceptions of reviews without delving into their ability to discern review authenticity in the first place. As a result of the fragmented literature, what has eluded scholarly attention is the extent to which consumers are able to perceive actual differences between authentic and fake reviews. To this end, the chapter highlights the theoretical value of weaving the two research strands together. With the aim to contribute to the theoretical discussion surrounding the problem, it specifically develops what is referred as the Theoretical model of Authentic and Fake reviews (the TAF). New research directions are identified based on the TAF.

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