scholarly journals Fake Product Monitoring and Removal for Genuine Product Feedback

2021 ◽  
Vol 7 (1) ◽  
pp. 1-3
Author(s):  
Mayuri Manikrao Patil ◽  
Snehal Nimba Nikumbh ◽  
Aparna Parshwanath Parigond
Keyword(s):  
Online Reviews ◽  
Sources Of Information ◽  
The Public ◽  
Fraud Risk ◽  
Network Analyses ◽  
User Behaviors ◽  
Opinion Spam ◽  
Using Data ◽  
Fraud Risk Management ◽  
Removal Model

A customer’s decision to purchase a product or service are primarily influenced by online reviews. Customers use online reviews, which are valuable sources of information to understand the public opinion on products and/or services. Dependability on online reviews can give rise to the potential concern that violator could give deceitful reviews in order to synthetically promote or decry products and services. This practice is known as Opinion Spam, where spammers manipulate reviews by making fake, untruthful, or deceptive reviews to get profit and boost their products, and devalue a competitor’s products. In order to tackle this issue, we propose to build a fraud risk management system and removal model. This captures fraudulent transactions based on user behaviors and network, analyses them in real-time using Data Mining, and accurately predicts the suspicious users and transactions. In this system, we use two algorithms NLP and TF-IDF to differentiate between fake and genuine reviews or feedback received by the customers

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 Public Sense of Water Fluoridation as Reflected on Twitter 2009–2017

2019 ◽  
Vol 99 (1) ◽  
pp. 11-17 ◽  
Author(s):  
H.J. Oh ◽  
C.H. Kim ◽  
J.G. Jeon
Keyword(s):  
Information Sources ◽  
The United States ◽  
Online Information ◽  
Water Fluoridation ◽  
The Public ◽  
Network Analyses ◽  
Current State ◽  
Twitter Users ◽  
Using Data ◽  
Negative Sentiment

Though controversial, water fluoridation has been hailed as one of the top-ten public-health achievements of the 20th century in the United States of America. In this article, we aim to investigate the public sense of water fluoridation as reflected on Twitter, using data from 2009 to 2017. To this end, tweets related to water fluoridation were collected using queries such as “fluoridated water or fluoride water,” “water fluoridation or fluoridation of water,” and hashtags related to water fluoridation. The collected tweets ( n = 218,748) were examined through informetric, linguistic (word sentiment, word frequency, and word network analyses), and issue tweet analyses. We found that Twitter users who tweeted about water fluoridation in English between 2009 and 2017 constituted about <0.01% of all users including non-English users. In their tweets, words such as “poison” and “waste” were the strong negative sentiment words most often used. Of the top 30 words most frequently used, words related to information sources on water fluoridation and the safety of water fluoridation appeared more often than words related to its efficacy. Additionally, the words related to information sources on water fluoridation and the safety of water fluoridation were found to be core terms in the sentences of tweet mentions. Our linguistic analyses indicate that Twitter users responded sensitively to words that emphasize negative aspects of fluoridation. This is clearly shown in our issue tweet analysis, where tweet mentions expressing negative opinions about water fluoridation accounted for at least 59.2% of all mentions. By contrast, <15% of tweet mentions were found to be positive. These findings suggest that professionals need to reevaluate the current state of online information about water fluoridation, and improve it in a way so that the public can easily access reliable information sources.

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.

Additional Evidence on Audit Report Lag

2001 ◽  
Vol 20 (1) ◽  
pp. 137-146 ◽  
Author(s):  
W. Robert Knechel ◽  
Jeff L. Payne
Keyword(s):  
Earnings Quality ◽  
Financial Statements ◽  
Financial Statement ◽  
Fiscal Year ◽  
Sources Of Information ◽  
Audit Report ◽  
The Public ◽  
Time Period ◽  
Audit Team ◽  
Available Information

The process for providing accounting information to the public has not changed much in the last century even though the extent of disclosure has increased signifi-cantly. Sundem et al. (1996) suggest that the primary benefit of audited financial statements may not be decision usefulness but the discipline imposed by timely confirmation of previously available information. In general, the value of information from the audited financial statement will decline as the audit report lag (the time period between a company's fiscal year end and the date of the audit report) increases since competitively oriented users may obtain substitute sources of information. Furthermore, the literature on earnings quality and earnings management suggests that unexpected reporting delays may be associated with lower quality information. The purpose of this paper is to extend our understanding about the determinants of audit report lag using a proprietary database containing 226 audit engagements from an international public accounting firm. We examine three previously uninvestigated audit firm factors that potentially influence audit report lag and are controllable by the auditor: (1) incremental audit effort (e.g., hours), (2) the resource allocation of audit team effort measured by rank (partner, manager, or staff), and (3) the provision of nonaudit services (MAS and tax). The results indicate that incremental audit effort, the presence of contentious tax issues, and the use of less experienced audit staff are positively correlated with audit report lag. Further, audit report lag is decreased by the potential synergistic relationship between MAS and audit services.

scholarly journals Estimation of the Actual Incidence of Coronavirus Disease (COVID-19) in Emergent Hotspots: The Example of Hokkaido, Japan during February–March 2020

2021 ◽  
Vol 10 (11) ◽  
pp. 2392
Author(s):  
Andrei R. Akhmetzhanov ◽  
Kenji Mizumoto ◽  
Sung-Mok Jung ◽  
Natalie M. Linton ◽  
Ryosuke Omori ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Surveillance System ◽  
Negative Binomial ◽  
Actual Number ◽  
Public Health Response ◽  
The Public ◽  
Health Response ◽  
Using Data ◽  
Insight Into ◽  
Actual Incidence

Following the first report of the coronavirus disease 2019 (COVID-19) in Sapporo city, Hokkaido Prefecture, Japan, on 14 February 2020, a surge of cases was observed in Hokkaido during February and March. As of 6 March, 90 cases were diagnosed in Hokkaido. Unfortunately, many infected persons may not have been recognized due to having mild or no symptoms during the initial months of the outbreak. We therefore aimed to predict the actual number of COVID-19 cases in (i) Hokkaido Prefecture and (ii) Sapporo city using data on cases diagnosed outside these areas. Two statistical frameworks involving a balance equation and an extrapolated linear regression model with a negative binomial link were used for deriving both estimates, respectively. The estimated cumulative incidence in Hokkaido as of 27 February was 2,297 cases (95% confidence interval (CI): 382–7091) based on data on travelers outbound from Hokkaido. The cumulative incidence in Sapporo city as of 28 February was estimated at 2233 cases (95% CI: 0–4893) based on the count of confirmed cases within Hokkaido. Both approaches resulted in similar estimates, indicating a higher incidence of infections in Hokkaido than were detected by the surveillance system. This quantification of the gap between detected and estimated cases helped to inform the public health response at the beginning of the pandemic and provided insight into the possible scope of undetected transmission for future assessments.

scholarly journals Punishment Strategies across Societies: Conventional Wisdoms Reconsidered

Games ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 63
Author(s):  
Ramzi Suleiman ◽  
Yuval Samid
Keyword(s):  
Public Goods ◽  
Strong Predictor ◽  
Social Environments ◽  
Public Goods Game ◽  
The Public ◽  
Free Riders ◽  
The World ◽  
Emergence Of Cooperation ◽  
Using Data ◽  
The Cost

Experiments using the public goods game have repeatedly shown that in cooperative social environments, punishment makes cooperation flourish, and withholding punishment makes cooperation collapse. In less cooperative social environments, where antisocial punishment has been detected, punishment was detrimental to cooperation. The success of punishment in enhancing cooperation was explained as deterrence of free riders by cooperative strong reciprocators, who were willing to pay the cost of punishing them, whereas in environments in which punishment diminished cooperation, antisocial punishment was explained as revenge by low cooperators against high cooperators suspected of punishing them in previous rounds. The present paper reconsiders the generality of both explanations. Using data from a public goods experiment with punishment, conducted by the authors on Israeli subjects (Study 1), and from a study published in Science using sixteen participant pools from cities around the world (Study 2), we found that: 1. The effect of punishment on the emergence of cooperation was mainly due to contributors increasing their cooperation, rather than from free riders being deterred. 2. Participants adhered to different contribution and punishment strategies. Some cooperated and did not punish (‘cooperators’); others cooperated and punished free riders (‘strong reciprocators’); a third subgroup punished upward and downward relative to their own contribution (‘norm-keepers’); and a small sub-group punished only cooperators (‘antisocial punishers’). 3. Clear societal differences emerged in the mix of the four participant types, with high-contributing pools characterized by higher ratios of ‘strong reciprocators’, and ‘cooperators’, and low-contributing pools characterized by a higher ratio of ‘norm keepers’. 4. The fraction of ‘strong reciprocators’ out of the total punishers emerged as a strong predictor of the groups’ level of cooperation and success in providing the public goods.

scholarly journals Paranoid styles and innumeracy: implications of a conspiracy mindset on Europeans' misperceptions about immigrants

2021 ◽  
pp. 1-17
Author(s):  
Sergio Martini ◽  
Mattia Guidi ◽  
Francesco Olmastroni ◽  
Linda Basile ◽  
Rossella Borri ◽  
...  
Keyword(s):  
Demographic Factors ◽  
European Countries ◽  
Democratic Accountability ◽  
Common Phenomenon ◽  
Political Issues ◽  
The Public ◽  
Using Data ◽  
Socio Demographic Factors ◽  
Influence Perceptions ◽  
Western Democracies

Abstract Innumeracy, that is, the inability to deal with numbers and provide correct estimates about political issues, is reported to be widespread among the public. Yet, despite the recognition that a conspiracy mindset is an increasingly common phenomenon in Western democracies, this has not been considered as a potential correlate of innumeracy. Using data from an online sample of respondents across 10 European countries, we show that those with a higher propensity to hold a conspiracy worldview tend to overestimate the actual share of the immigrant population living in their own country. This association holds true when accounting for country heterogeneity and other cognitive, affective and socio-demographic factors. Employing a comparative design and refined measurements, the article contributes to our understanding of how a conspiracy mentality may influence perceptions of relevant political facts, questioning basic processes of democratic accountability.

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.

A Psychological Process of Bureaucratic Whistleblowing: Applying the Theory of Planned Behavior

2021 ◽  
pp. 027507402110033
Author(s):  
Hongseok Lee ◽  
Minsung Michael Kang ◽  
Sun Young Kim
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Behavioral Control ◽  
Perceived Behavioral Control ◽  
Psychological Process ◽  
Public Employees ◽  
The Public ◽  
Using Data

Whistleblowing is a psychological process that involves the calculation of risks and benefits. While there exists a broad range of research on whistleblowing in the public sector, previous studies have not examined its entire process due to the limited focus on either whistleblowing intention or whistleblowing behavior. This study aims to fill this gap by applying the theory of planned behavior (TPB) to the whistleblowing context. Specifically, we examine how individual beliefs about the likely consequences of whistleblowing (attitude toward whistleblowing), others’ expectations about whistleblowing (subjective norm), and the capability of blowing the whistle (perceived behavioral control) influence public employees’ actual whistleblowing by way of their intention to report wrongdoings. A series of structural equation models are tested using data from the 2010 Merit Principles Survey. The findings show that the more the employees perceive that the consequences of whistleblowing are important, the more the key referents support whistleblowing, and the more the protections for whistleblowers are available, the more likely are their intentions to disclose wrongdoings and then actually engage in whistleblowing behavior. We conduct additional analyses for internal and external whistleblowers separately and find that there are both meaningful similarities and differences between the two groups. This study provides support for the validity of TPB as a theoretical framework for better understanding and explicating the psychological process of bureaucratic whistleblowing.

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