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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.

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Detecting Fake Reviews Using Multidimensional Representations With Fine-Grained Aspects Plan

IEEE Access ◽

10.1109/access.2020.3047947 ◽

2021 ◽

Vol 9 ◽

pp. 3765-3773

Author(s):

Meiling Liu ◽

Yue Shang ◽

Qi Yue ◽

Jiyun Zhou

Keyword(s):

Fine Grained ◽

Fake Reviews

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THE SOCIAL TV PHENOMENON AND FAKE ONLINE RESTAURANT REVIEWS

Tourism and hospitality management ◽

10.20867/thm.27.1.2 ◽

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.

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Classification of Fake Product Ratings Using a Timeline Based Approach

International Journal of Business Administration and Management Research ◽

10.24178/ijbamr.2017.3.2.12 ◽

2017 ◽

Vol 3 (2) ◽

pp. 12 ◽

Cited By ~ 1

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.

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Role of Community Medicine during COVID-19 Pandemic

Indian Journal of Community Health ◽

10.47203/ijch.2021.v33i02.003 ◽

2021 ◽

Vol 33 (2) ◽

pp. 242-244

Author(s):

Epari Venkata Rao ◽

Prem Sagar Panda

Keyword(s):

Public Health ◽

Health Care Services ◽

Community Medicine ◽

Public Health Services ◽

Health Crisis ◽

Public Health Expert ◽

The Public ◽

Care Services ◽

Health Expert

Pandemics have significantly affected economy of each country. Health & political system have been also drastically affected in each part of the country. To fight against pandemic, it demands multidimensional approaches comprising of various measures like surveillance, containment, isolation & quarantine, border restriction as well as various socio-political and community measures. Though the entire health workforce is involved at multiple levels, the role of a community medicine/public health expert is maximum in controlling the spread in the community and managing the situation. The community medicine specialists can contribute to the public health as well as health-care services in combating the pandemic. This review has been done for giving an insight of proper utilisation of public health services and existing manpower of community medicine. Also this will channelize our health system and give a direction for combating future public health crisis. So Government should utilise the experiences and expertise to manage the pandemic very well.

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