A Study on Opinion Spamming: Fake Consumer Review Detection

SN Social Sciences, vol. 1:181, 2021, pp. 1-30. , 2021

User-generated online reviews have become essential sources of information for potential consumers. Given the increasing importance of online reviews on consumer purchases, some traders try to publish or promote fake reviews on online platforms to improve the reputation of their goods or services, or to damage the reputation of their competitors. This paper aims (1) to analyse whether and how fake reviews violate the main legal and ethical principles of advertising, according to the legislations of the US, UK and EU as well as the International Advertising and Marketing Communications Code; (2) to assess how the EU, the UK and the US are dealing with them, and find out which approaches and actions are working best; and (3) to offer normative recommendations for effective prosecution of fake reviews. The paper uses a combination of theoretical and sociological approaches (section 1), black letter analysis of law (sections 2 and 3) and a comparative approach (sections 2 and 3). Among others, the article observes (1) that fake reviews are a form of illegal advertising and (2) that the European regulatory instruments provide more protection against fake reviews than the US and argues (3) that an effective fight against fake reviews requires strong administrative bodies, endowed with sufficient resources.

With the rise of online business, consumers nowadays are not only able do their shopping online, but also they can leave reviews on their purchased products for other potential users to see. There is also potential for vendors trying to influence their consumers' decisions, by injecting deceptive product reviews online. Many efforts have been done recently to develop algorithms to detect such deceptive opinion spam. Throughout this report, we will try to shed the light on some of the work done in this area. I wrote this report as part of my MSc. degree in data mining program in the University of East Anglia.

Proceedings of the international conference on …, 2008

Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them.

Artificial Intelligence Review, 2019

Consumer reviews are considered to be of utmost significance in the field of e-commerce, for they have a stronghold in deciding the revenue of a business. When arriving at a purchasing decision, a majority of online consumers rely on reviews since they offer credible means of mining opinions of other consumers regarding a particular product. The trustworthiness of online reviews directly affects a company's reputation and profitability, which is why certain business owners pay fraudsters to generate deceptive reviews. Such generation of deceptive reviews which manipulate the purchasing decision of consumers is a persistent and harmful issue. Hence, developing methods to assist businesses and consumers by distinguishing between credible reviews and deceptive reviews remains to be a crucial, yet challenging task. In view of that, this paper unravels prominent techniques that have been proposed to solve the issue of deceptive review detection. Accordingly, the primary goal of this paper is to provide an in-depth analysis of current research on detecting deceptive reviews and to identify the characteristics, strengths, and bottlenecks of those methodologies which may need further improvements.

Central and Eastern European eDem and eGov Days

Over the past few years, we have seen a rise in spoofed content across the Internet, including fake news and fake reviews. Fake news is false information that imitates real news intending to damage a reputation or make financial gains. Similarly, fake online reviews, commonly referred to as opinion spamming, have impacted customers and retailers. The main problem is that there is no efficient way to distinguish between genuine and fake reviews; even humans find it difficult to do so. This paper compares two tools used to detect opinion spamming and analyzes their results. We have compiled our research using 100 reviews from travelers who evaluated Istanbul's top five best-value hotels.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

In today’s world reviews play a vital role in online shopping and sales of the product because people consider all the pros and cons of any product before they buy it as there are many different options for the same product as well as different manufacturers for the same type of product with the difference in quality

2010

Abstract This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products.

2012

Consumers' purchase decisions are increasingly influenced by user-generated online reviews . Accordingly, there has been growing concern about the potential for posting deceptive opinion spam-fictitious reviews that have been deliberately written to sound authentic, to deceive the reader . But while this practice has received considerable public attention and concern, relatively little is known about the actual prevalence, or rate, of deception in online review communities, and less still about the factors that influence it.

JOURNAL OF HARBIN INSTITUTE OF TECHNOLOGY, 2022

User-generated reviews can have a substantial impact on an organization's income in e-commerce. When making a purchase or deciding on a service or product, online customers depend on the opinions of others. Thus, e-commerce product reviews' trustworthiness is critical for organizations, and it can have a direct impact on their reputation and revenue. As a result, some online purchasing and selling companies employ fraudsters to create bogus or fake opinions or reviews on their websites. The objective of fake reviews is to mislead customers into making the wrong purchasing decision. In the last two decades, different methods of detecting fraudulent reviews have been intensively investigated. However, there is still a lack of the literature surveys that can really investigate and summaries the current methods and challenges facing fake opinions detection. In order to tackle this problem, this survey sums up the publicly available datasets and their gathering methods for the detection of fraudulent reviews. It examines the methods that are currently in use for feature engineering for fake review analysis, as well as deep learning and classical machine learning that have been implemented for fake review classification and finds any inconsistencies and limitations.

IRJET, 2021

The branding and promotion of product reviews is critical for online stores. They help build trust and loyalty, and typically establish the difference between your products and others. Since consumer interest could disrupt, fake reviews are a big challenge to websites and product search engines. While fake evaluations are good to everyone in the long run, the rise in this doubtful tactic is very fascinating. The fact that huge amounts of time, energy and resources are being poured into fake review spamming is an indicator of just how valuable it can be to have a decent number of reviews. In this manuscript, a conventional method is proposed for elimination of Fake Reviews on ecommerce sites. The solution is to use the reviewers' behavior features with review parameters such as the username, IP address and duplicate reviews to delete fake users.