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 Cloud based Framework for Fake Review Detection

2019 ◽  
pp. 9-12
Author(s):  
Md. Towhidul Islam Robin
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Cloud Storage ◽  
English Language ◽  
Online Reviews ◽  
Purchase Decision ◽  
Business Analytics ◽  
Fake Reviews ◽  
Outcome Result ◽  
Significant Factors

Online reviews are one of the significant factors in a customer’s purchase decision or to avail of any service. Online reviews give rise to the potential threats that fake reviewers may write a false review to artificially promote a product or defaming value of a service. Using Natural Language Processing, many methods have already been developed to detect fake reviews, especially reviews written in the English language. In this paper, I propose a novel framework where authenticity of a feedback will check through two perspectives. Firstly, the system checks whether the review is fake or not. Secondly, it also checks the authenticity of the reviewer. The outcome result accumulates in cloud storage for providing further business analytics.

A Proposed Solution for Identifying Online Fake Reviews in the Research Process

2021 ◽  
pp. 188-205
Author(s):  
Victor-Alexandru Briciu ◽  
Cristian-Laurențiu Roman ◽  
Arabela Briciu
Keyword(s):  
Online Reviews ◽  
Research Process ◽  
Purchase Decision ◽  
Specialized Software ◽  
Fake Reviews ◽  
A Company

This chapter aims to present the issue of manipulation of online reviews, behind which there is always an interest, whether it is about increasing sales, promoting a product, degrading the image of a competing brand or product. Such reviews can influence the purchase decision or the sales of a company. Combining users' text with their behavior has yielded the best results in identifying fake reviews, and this remains probably the most effective method to date. The chapter proposes, as a novelty factor, a methodological solution before analyzing reviews through specialized software (e.g., SmartMunk, Revuze, Aspectiva, SentiGeek, etc.), a filter for identifying fake reviews by introducing them into a fake review application called Fakespot. Moreover, the idea that these false reviews can influence the purchase decision of customers in any field is emphasized, so it is very important that large companies develop programs or systems that detect them.

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.

scholarly journals Online Fraud Review Detection Using Data Mining

2020 ◽  
pp. 326-330
Keyword(s):  
Detection System ◽  
Online Reviews ◽  
Automated Detection ◽  
Product Reviews ◽  
Online Fraud ◽  
The People ◽  
Or Groups ◽  
Opinion Spam ◽  
Using Data ◽  
Fake Reviews

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.

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.

“We Are All Humans and Deserve a Decent Way to Go”: Examining Professional’s Experiences With Providing End-of-Life Care in Correctional Institutions

2021 ◽  
pp. 073401682110208
Author(s):  
Mollee Steely Smith ◽  
Brooke Cooley ◽  
Tusty ten Bensel
Keyword(s):  
End Of Life ◽  
End Of Life Care ◽  
Service Providers ◽  
Prison Population ◽  
Correctional Institutions ◽  
Southern State ◽  
Face To Face ◽  
The Past ◽  
Eol Care ◽  
Emotional Effects

The aging prison population has increased dramatically over the past two decades. As this population increases, correctional institutions are faced with health care challenges. Specifically, providing adequate end-of-life (EOL) care for terminally ill inmates has been a concern. Despite issues relating to providing EOL care, little is known about medical and correctional staff’s attitudes toward the implementation of EOL care. The purpose of this study was to understand the challenges faced by correctional and medical professionals, focusing on job satisfaction, obstacles, and emotional effects of providing EOL care in correctional institutions. Our data included 17 semistructured, face-to-face interviews with medical and correctional staff assigned to the EOL care unit in a southern state. Although the entire sample stated overall satisfaction with their job, participants noted several challenges and stressors, which included the lack of resources and difficulties in balancing care. Participants agreed that it was emotionally stressful to maintain appropriate relationships with the inmates, deal with patient manipulation, and be surrounded by dying and death. Implications are discussed relative to the needs and experiences of service providers and how to more effectively treat EOL inmate patients.

scholarly journals Consumer Trust as the Antecedent of Online Consumer Purchase Decision

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 145
Author(s):  
Anas Hidayat ◽  
Tony Wijaya ◽  
Asmai Ishak ◽  
Putra Endi Catyanadika
Keyword(s):  
Questionnaire Survey ◽  
Service Providers ◽  
Perceived Value ◽  
Partial Least Square ◽  
Least Square ◽  
Consumer Trust ◽  
Purchase Decision ◽  
Internet Users ◽  
Online Consumer

The e-commerce industry in Indonesia is growing in line with the increasing number of internet users in Indonesia. Unfortunately, many internet users in Indonesia are still unsure about shopping online because of the lack of buyer trust with sellers and service providers. This study aims to identify the factors that influence online shop consumers to conduct transactions online. This research used a questionnaire survey distributed to customers who had ever used an online shop application. The sample used in this research was 468 respondents. The data collected was then analyzed using Partial Least Square. The results of this research indicated that trust, perceived value, and buying interest positively influence consumers’ decisions to purchase using an online shop application.

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 Exploring the behavioral drivers of review valence

2018 ◽  
Vol 30 (10) ◽  
pp. 3083-3099 ◽  
Author(s):  
Panagiotis Stamolampros ◽  
Nikolaos Korfiatis
Keyword(s):  
Information Content ◽  
Service Providers ◽  
Online Reviews ◽  
The Other ◽  
Three Dimensions ◽  
Spatial Distance ◽  
Bag Of Words ◽  
Content Type ◽  
Online Ratings ◽  
Positive Direct

Purpose Although the literature has established the effect of online reviews on customer purchase intentions, the influence of psychological factors on online ratings is overlooked. This paper aims to examine these factors under the perspective of construal level theory (CLT). Design/methodology/approach Using review data from TripAdvisor and Booking.com, the authors study three dimensions of psychological distances (temporal, spatial and social) and their direct and interaction effects on review valence, using regression analysis. The authors examine the effect of these distances on the information content of online reviews using a novel bag-of-words model to assess its concreteness. Findings Temporal distance and spatial distance have positive direct effects on review valence. Social distance, on the other hand, has a negative direct effect. However, its interaction with the other two distances has a positive effect, suggesting that consumers tend to “zoom-out” to less concrete things in their ratings. Practical implications The findings provide implications for the interpretation of review ratings by the service providers and their information content. Originality/value This study extends the CLT and electronic word-of-mouth literature by jointly exploring the effect of all three psychological distances that are applicable in post-purchase evaluations. Methodologically, it provides a novel application of the bag-of-words model in evaluating the concreteness of online reviews.

scholarly journals Critical factors that impact on the efficiency of the Lagos seaports

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Francis Ojadi ◽  
Jackie Walters
Keyword(s):  
Value Chain ◽  
Mixed Method ◽  
Service Providers ◽  
Transport Infrastructure ◽  
State Agencies ◽  
Mixed Method Research ◽  
Critical Factors ◽  
The Past ◽  
Container Depots ◽  
And Storage

Background: Since the past two decades, the Lagos seaports have experienced vessel and storage yard cargo congestion, resulting in dwell times of about 30 days for containerised imports and high trade logistics costs.Objectives: The purpose of this study was to identify the critical factors that impact the operational efficiency of the Lagos seaports with a view to improving liner trade activities.Method: The study adopted an operational-based approach to understand the dynamics of the various interfaces of the port value chain. The research paradigm adopted for the study was therefore a combination of constructivism and post-positivism paradigms, which entailed the exploration and understanding of the various stakeholders in the port value chain. The epistemology of the research relied on the use of the exploratory sequential mixed method research technique (i.e. the qualitative approach followed by the quantitative approach) at the operational level of port operations.Results: The result of the research showed that significant challenges exist and that some of these challenges cut across all functions of port operations. Challenges are experienced in the areas of corruption, trade fraud, transport infrastructure deficits, the absence of a supply chain culture and shortcomings in the execution of the ‘contract of customs’. Additionally, these factors include the deficiencies in services and facilities provided by state agencies and government-appointed service providers and private sector companies such as truckers, inland container depots, Inland Container Depots (ICDs) and terminal operators.Conclusion: Specific recommendations are made to address the issues identified which, if implemented, could significantly address the current inefficiencies observed in the Lagos seaport’s operations.

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