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.

scholarly journals CREMOR: CREdibility Model on Online Reviews-How people Consider Online Reviews Believable

2017 ◽  
Vol 10 (7) ◽  
pp. 56 ◽  
Author(s):  
Patrizia Grifoni ◽  
Fernando Ferri ◽  
Tiziana Guzzo
Keyword(s):  
Purchase Intention ◽  
Source Credibility ◽  
Online Reviews ◽  
Online Information ◽  
Perceived Credibility ◽  
Product Service ◽  
Fake Reviews ◽  
The Impact ◽  
The Web

The Internet is deeply changing how buyers and sellers interact in the marketplace. The Web enables consumers to be informed on their purchases both online and offline thanks to crowdsourced reviews. However, recent studies have found evidence that online consumers review could be not truthful as some users such as owners, competitors, paid users, sometimes post fake reviews. In this context the question of credibility is becoming more and more relevant in the Web 2.0 environment in which the concepts of social influence and electronic word of mouth are acquiring a great importance. The user’s perception of online reviews can influence source credibility and the perception of the quality of a product/service, as well as the likelihood that someone will purchase the product/service. This study proposes a model that analyses elements that influence online information credibility and the impact of the perceived credibility on purchase intention.

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.

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.

The effect of emotions, eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services

2020 ◽  
Vol 31 (3) ◽  
pp. 465-487 ◽  
Author(s):  
Carla Ruiz-Mafe ◽  
Enrique Bigné-Alcañiz ◽  
Rafael Currás-Pérez
Keyword(s):  
Structural Model ◽  
Online Reviews ◽  
Moderating Effect ◽  
Online Information ◽  
Bias Effect ◽  
Content Type ◽  
Digital Services ◽  
Information Cues ◽  
Cognitive Information ◽  
The Impact

PurposeThis paper analyses the interrelationships between emotions, the cognitive information cues of online reviews and intention to follow the advice obtained from digital platforms, paying special attention to the moderating effect of the sequencing of review valence.Design/methodology/approachThe data were collected from 830 Spanish Tripadvisor users. In a two-step approach, a measurement model was estimated and a structural model analysed to test the proposed hypotheses. SmartPLS 3.0 software was used. The moderating effect of sequencing of reviews is tested.FindingsThe data analysis showed a bias effect of review sequence on the impact of online information cues and emotions on intention to follow advice obtained from Tripadvisor. When the online reviews of a restaurant begin with positive commentaries, their perceived persuasiveness is a stronger driver of the pleasure and arousal elicited by online reviews than when they begin with negative reviews. On the other hand, the perceived helpfulness of online reviews only triggers arousal when the user reads negative, followed by positive, comments. The impact of pleasure on intention to follow the advice provided in an online travel community is higher with positive-negative than with negative-positive sequences.Originality/valueWhile researchers have demonstrated the benefits of customer reviews on company sales, a largely uninvestigated issue is the interplay between emotions and cognitive information cues in the processing of online reviews. This is one of the first studies to examine the moderating effect of conflicting reviews on the impact of emotions and cognitive information cues on consumer intention to follow the advice obtained from digital services.

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.

Business Data Analytics Applications to Online Product Reviews and Nationalism

2021 ◽  
Vol 2 (2) ◽  
pp. 27-39
Author(s):  
Charles C. Willow
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Smart Phone ◽  
Consumer Preference ◽  
Online Reviews ◽  
Global Market ◽  
Product Reviews ◽  
Purchase Decisions ◽  
Control Factors

This paper investigates the data analytics between consumer purchase decisions relative to the on-line reviews. The multi-attributes associated with purchase decisions are comprised of nationalism and consumer preference to be correlated with online reviews using big data analytics. By far, a small fraction of meaningful studies have sought to correlate nationalism and ethnocentrism with big data analytics to date. Globally accepted generic products are selected to expedite the process of data engineering. Two sets were arranged: passenger automobiles for transportation with an estimated $9 trillion global market and the smart phone, boosting its market size of approximately $5 billion. Both products provide minimized cultural, linguistic, gender, age, and/or custom barriers of entry for prospective digital consumers, thereby allowing relatively unrestricted engagement with online reviews and purchases. A series of hypothesis tests indicate that there is a positive correlation between nationalism and automobiles. As to smart cell phones, however, nationalism had nominal control factors. Multi-variate analytics were performed by using R and Tableau Public.

THE SOCIAL TV PHENOMENON AND FAKE ONLINE RESTAURANT REVIEWS

2021 ◽  
Vol 27 (1) ◽  
pp. 25-42
Author(s):  
Breno de Paula Andrade Cruz ◽  
Susana C. Silva ◽  
Steven Dutt Ross
Keyword(s):  
Online Reviews ◽  
Assessment System ◽  
Reality Show ◽  
Social Tv ◽  
The Social ◽  
Restaurant Reviews ◽  
Brazilian Culture ◽  
Fake Reviews ◽  
Tv Show ◽  
Practical Implications

Purpose – The social TV phenomenon has raised the interest of some researchers in studying the production of online reviews. However, little is known about the characteristics of reviewers that, without having had indeed a real experience of consumption, still dare to assess the service. The purpose of this research is to understand these reviewers better, using an experiment conducted in Brazil. Design/methodology/approach – Through a cluster analysis with 2547 reviewers of 7 restaurants that participated in a reality show in Brazil, we were able to create 4 fours. Using Spearman Correlation and Kruskal-Wallis Test, differences among groups were analysed in the search of behavioural changes among different types of reviewers. Findings – We conclude that social TV influence fake online reviews of restaurants that were involved in a tv show. Furthermore, we were able to verify that some reviewers indeed assess the service without indeed having tried the service, which strongly bias the influence they are going to cause in potential consumers. Four types of reviewers were identified: the real expert, the amateur reviewer, the speculator and the pseudo expert. The 2 latter types are analyzed through the anthropologic lens of the popular Brazilian culture and the TV influence in that country. Research limitations/implications – we were able to understand how TV can influence the construction of fake online reviews for restaurants. Practical implications – It is important for the restaurant and hospitality industry in general, to be able to be attentive to the phenomenon of fake reviews that can totally biased the advantages of this assessment system that was created to produce trust among consumers, but that can act exactly the other way around. Originality/value – This study highlights the relevance of taking into account cultural background of the country where the restaurant is located, as well as emphasizing the relevance of conducting a previous analysis of the decision of embarking on a reality show that it has high chances to biasedly influence consumers’ decisions.

scholarly journals Classification of Fake Product Ratings Using a Timeline Based Approach

2017 ◽  
Vol 3 (2) ◽  
pp. 12 ◽  
Author(s):  
Neha Thomas ◽  
Susan Elias
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Optimal Point ◽  
Linear Classifiers ◽  
Wide Range ◽  
Text Content ◽  
Classification Tool ◽  
Fake Reviews ◽  
Product Ratings

 Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review.  In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years.  The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product.    Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.

scholarly journals Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations

2018 ◽  
Author(s):  
Xiaoying Zhang ◽  
Hong Xie ◽  
Junzhou Zhao ◽  
John C.S. Lui
Keyword(s):  
Product Quality ◽  
Factor Model ◽  
Experimental Studies ◽  
Purchase Decisions ◽  
Contrast Effects ◽  
Rating Systems ◽  
Micro Level ◽  
Online Product Ratings ◽  
Product Ratings ◽  
Product Rating

The unbiasedness of online product ratings, an important property to ensure that users’ ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” the distortions from historical ratings in each single rating (or at the micro-level), and perform the “debiasing operations” in real rating systems are the main objectives of this work. Using 42 million real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the “Assimilate-Contrast” theory. However, none of the existing works on modeling historical ratings’ influence have taken this into account, and this motivates us to propose the Histori- cal Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users’ real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.

The impact of monetary rewards for online reviews

2019 ◽  
Vol 31 (5) ◽  
pp. 1486-1515 ◽  
Author(s):  
Yongrui Duan ◽  
Chen Chen ◽  
Jiazhen Huo
Keyword(s):  
Analytical Framework ◽  
Online Reviews ◽  
Monetary Reward ◽  
Product Reviews ◽  
Purchase Decisions ◽  
Content Type ◽  
Second Stage ◽  
Monetary Rewards ◽  
Stage 1 ◽  
The Impact

Purpose To encourage buyers to contribute product reviews, some online sellers offer monetary rewards. The purpose of this paper is to investigate the impact of monetary rewards on buyers’ purchase decisions and review contributions, as well as the impact on the seller’s price decisions and profit. Design/methodology/approach The authors consider an online seller in a two-stage setting. Prior to Stage 1, the profit-maximizing seller sets the price and decides whether to offer a monetary reward secretly to motivate online reviews. Then, a continuum of buyers arrives and makes purchase decisions at the beginning of each stage. First-stage buyers may contribute reviews if they are satisfied, which will affect demand in the second stage. Using this analytical framework, the authors analyze the impact of monetary rewards. Findings If the monetary reward is small, it decreases the seller’s profit and fails to generate more reviews. It also increases price, leading to a decline in total demand. Thus, when the reward is lower than a certain threshold, all buyers are worse off. Only when the reward exceeds the threshold are buyers who contribute reviews better off. Profit and total demand both increase in review quality, while the price may either increase or decrease in it. Originality/value To the best of the authors’ knowledge, this paper is the first to analyze theoretically the impact of monetary rewards on buyers’ purchase decisions, review contributions and on online sellers’ decisions.

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