DETECTING AND CHARACTERIZING EXTREMIST REVIEWER GROUPS IN ONLINE PRODUCT REVIEWS

Online marketplaces often witness opinion spam in the form of reviews. People are often hired to target specific brands for promoting or impeding them by writing highly positive or negative reviews. This often is done collectively in groups. Although some previous studies attempted to identify and analyze such opinion spam groups, little has been explored to spot those groups who target a brand as a whole, instead of just products. In this article, we collected the reviews from the Amazon product review site and manually labeled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed (products of) a lot of brands. We hypothesize that the nature of the reviewer groups is dependent on eight features specific to a (group, brand) pair. We develop a feature-based supervised model to classify candidate groups as extremist entities. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group to determine whether the group shows signs of extremity. A three-layer perceptron-based classifier turns out to be the best classifier. We further study behaviors of such groups in detail to understand the dynamics of brand-level opinion fraud better. These behaviors include consistency in ratings, review sentiment, verified purchase, review dates, and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which, on further investigation, leads to ways to circumvent the existing mechanisms in place to prevent unofficial incentives on Amazon

Identifying and Profiling Radical Reviewer Collectives in Digital Product Reviews

Ecommerce sites are flooded with spam reviews and opinions. People are usually hired to impede or promote particular brands by writing extremely negative or positive reviews. It is usually performed in groups. Various studies have been conducted to identify and scan those spam groups. However, there is still a knowledge gap when it comes to detecting groups targeting a brand, instead of products only. In this study, we conducted a systematic review of recent studies related to detection of extremist reviewer groups. Most of the researchers have extracted these groups with a data mining approach over brand similarities so that users are clustered. This study is an attempt to detect spammers with various models tested by various reviewers. This study presents proven conceptual models and algorithms which have been presented in previous studies to compute the spamming level of extremist reviewers in ecommerce sites and online marketplace

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Barriers and Drivers for Electric Vehicle Adoption in Developing Countries

Electric vehicles (EVs) present a promising solution for addressing environmental concerns and promoting sustainable urban mobility in developing countries. However, several barriers hinder their widespread adoption. This study investigates the barriers and drivers influencing EV adoption in developing countries, with a focus on the following factors: high upfront cost, limited charging infrastructure, limited availability and variety of EV models, range anxiety, and lack of awareness and education. The high upfront cost of EVs poses a significant barrier to adoption, as it may exceed the purchasing power of consumers in developing countries. Furthermore, the limited availability of public charging stations contributes to range anxiety and discourages potential EV buyers. Additionally, the lack of diverse EV models in the market limits consumer choice and may deter those with specific requirements or preferences. Lack of awareness and education about EV technology, charging options, and government incentives further hinder adoption. On the other hand, several drivers can promote EV adoption. The environmental benefits of EVs, such as reduced air pollution and greenhouse gas emissions, resonate with the growing awareness and concern for environmental issues in many developing countries. Supportive government policies and incentives, including subsidies, tax benefits, and exemptions, can significantly influence the financial attractiveness of EVs. Rising fuel costs and the volatility of imported fossil fuel prices provide an additional motivation to shift to EVs as a more stable and potentially cheaper source of energy. Technological advancements and economies of scale in the EV industry are expected to decrease the cost of EVs, making them more affordable and attractive to consumers.

The Impact of Big Data on Health Economics: Opportunities and Applications

Big Data has transformed the field of health economics, providing researchers with an unprecedented level of data and insights that can inform healthcare policy and practice. In this study, we explored the opportunities and applications of Big Data in health economics, examining its potential to improve healthcare delivery, reduce costs, and promote better health outcomes. Our findings suggest that Big Data has significant potential to transform thne field of health economics. By using predictive analytics, health economists can identify patterns and trends in healthcare utilization, cost, and outcomes, which can inform the design and implementation of more effective and cost-efficient interventions. Additionally, Big Data can be used to develop personalized treatment plans that are tailored to an individual's specific needs, reducing healthcare costs and improving patient outcomes. Furthermore, Big Data can be used to monitor and manage population health by identifying high-risk individuals, predicting disease outbreaks, and developing strategies to prevent and manage chronic conditions. Health economists can also use Big Data to evaluate the impact of health policy interventions, such as Medicaid expansion and value-based care, and inform future policy decisions. Our study demonstrates that Big Data presents numerous opportunities for health economists to improve healthcare delivery, reduce costs, and promote better health outcomes. By leveraging the power of Big Data, health economists can develop new insights and strategies that can transform the field of health economics and benefit patients, providers, and policymakers alike

ARTIFICIAL INTELLIGENCE'S EFFECT ON NETWORKING TECHNOLOGY IN THE FUTURE

Artificial Intelligence is the science and engineering of making intelligent machines, aimed at providing machines with the ability to think, reach, and surpass human-level intelligence. In this paper, we begin with an introduction to the general field of artificial intelligence, then progress to the birth, history, and the rise of artificial intelligence. We then explore the main streams in the field, along with the advancement, evolution, and its applications for various aspects of our life. The paper will cover central and current research related to artificial intelligence, including reinforcement learning, robotics, computer vision, and symbolic logic. In parallel, we highlight the unique advantages for future technologies, focusing on opportunities, limitations, and ethical questions. To conclude, we describe several current areas of research within the field and recommendations for future research

Internet Filters: A Public Policy Report (Second edition; fully revised and updated)

No sooner was the Internet upon us than anxiety arose over the ease of accessing pornography and other controversial content. In response, entrepreneurs soon developed filtering products. By the end of the decade, a new industry had emerged to create and market Internet filters....Yet filters were highly imprecise from the beginning. The sheer size of the Internet meant that identifying potentially offensive content had to be done mechanically, by matching "key" words and phrases; hence, the blocking of Web sites for "Middlesex County," or words such as "magna cum laude". Internet filters are crude and error-prone because they categorize expression without regard to its context, meaning, and value. Yet these sweeping censorship tools are now widely used in companies, homes, schools, and libraries. Internet filters remain a pressing public policy issue to all those concerned about free expression, education, culture, and democracy. This fully revised and updated report surveys tests and studies of Internet filtering products from the mid-1990s through 2006. It provides an essential resource for the ongoing debate