An Empirical Study of Online Consumer Review Spam: A Design Science Approach

Because of the sheer volume of consumer reviews posted to the Internet, a manual approach for the detection and analysis of fake reviews is not practical. However, automated detection of fake reviews is a very challenging research problem given the fact that fake reviews could just look like legitimate reviews. Guided by the design science research methodology, one of the main contributions of our research work is the development of a novel methodology and an instantiation which can effectively detect untruthful consumer reviews. The results of our experiment confirm that the proposed methodology outperforms other well-known baseline methods for detecting untruthful reviews collected from amazon.com. Above all, the designed artifacts enable us to conduct an econometric analysis to examine the impact of fake reviews on product sales. To the best of our knowledge, this is the first empirical study conducted to analyze the economic impact of fake consumer reviews

A Critical Analysis Of The State-Of-The-Art On Automated Detection Of Deceptive Behavior In Social Media

Recently, a large body of research has been devoted to examine the user behavioral patterns and the business implications of social media. However, relatively little research has been conducted regarding users’ deceptive activities in social media; these deceptive activities may hinder the effective application of the data collected from social media to perform e-marketing and initiate business transformation in general. One of the main contributions of this paper is the critical analysis of the possible forms of deceptive behavior in social media and the state-of-the-art technologies for automated deception detection in social media. Based on the proposed taxonomy of major deception types, the assumptions, advantages, and disadvantages of the popular deception detection methods are analyzed. Our critical analysis shows that deceptive behavior may evolve over time, and so making it difficult for the existing methods to effectively detect social media spam. Accordingly, another main contribution of this paper is the design and development of a generic framework to combat dynamic deceptive activities in social media. The managerial implication of our research is that business managers or marketers will develop better insights about the possible deceptive behavior in social media before they tap into social media to collect and generate market intelligence. Moreover, they can apply the proposed adaptive deception detection framework to more effectively combat the ever increasing and evolving deceptive activities in social medi

Internet-based Multiagent Architecture

Research in intelligent agents and multiagent systems that run on the Internet has received increased attention and importance in recent years. Since the Internet continues to grow, intelligent agent technology is progressively being introduced to many Internet-based applications for communications between different applications. The aim of this paper is to present an Internet-based architecture for multiagent systems which offers a communication infrastructure and coordination services for agents to achieve their goals. A structured architecture is proposed to support communication facilities among several agents and coordinate agent activities in distributed environments such as the Internet and Intranets. The architecture consists of 1) Application agents, 2) Communication handler, 3) Knowledge manager, and 4) Repository. (Yuen, et al. 1999; Leung, et al. 1999). These four layers cooperate together and provide common facilities necessary for typical multiagent systems or agent-based applications with various choices. An Internet-based prototype for auditing and detecting unauthorized transactions within an organization over the Internet or an Intranet is implemented to demonstrate the practicability and feasibility of the proposed Internet-based architecture for multiagent systems

A Month in the Life of Groupon

Groupon has become the latest Internet sensation, providing daily deals to customers in the form of discount offers for restaurants, ticketed events, appliances, services, and other items. We undertake a study of the economics of daily deals on the web, based on a dataset we compiled by monitoring Groupon over several weeks. We use our dataset to characterize Groupon deal purchases, and to glean insights about Groupon's operational strategy. Our focus is on purchase incentives. For the primary purchase incentive, price, our regression model indicates that demand for coupons is relatively inelastic, allowing room for price-based revenue optimization. More interestingly, mining our dataset, we find evidence that Groupon customers are sensitive to other, "soft", incentives, e.g., deal scheduling and duration, deal featuring, and limited inventory. Our analysis points to the importance of considering incentives other than price in optimizing deal sites and similar systems.Comment: 6 page

Student Profiling System for an Agent-Based Educational System

Web-based educational systems are receiving more and more attention, because of the explosive growth of the Internet and World Wide Web. However, such kinds of self-study systems still lack several aspects, compared to a real life classroom setting and so suffer from their passive nature. They are often dull and plodding and leave students unmotivated. Unlike human teachers, these systems are not able to know individual students, to identify students’ learning problem and to provide tailored aids to specific students. The major objective of our project is to establish a student profiling system that provides storage of learning and an interaction history for each individual student who has used a web-based teaching system. Our system will provide the functions of recording students’ learning activities, providing webbased assessments to students, measuring students’ academic performance, and allowing teachers to analyze students’ activities

Search for an axion-like particle in radiative J/ψ decays

We search for an axion-like particle (ALP) a through the process ψ(3686)→π+π−J/ψ, J/ψ→γa, a→γγ in a data sample of (2.71±0.01)×109 ψ(3686) events collected by the BESIII detector. No significant ALP signal is observed over the expected background, and the upper limits on the branching fraction of the decay J/ψ→γa and the ALP-photon coupling constant gaγγ are set at 95% confidence level in the mass range of 0.165≤ma≤2.84GeV/c2. The limits on B(J/ψ→γa) range from 8.3×10−8 to 1.8×10−6 over the search region, and the constraints on the ALP-photon coupling are the most stringent to date for 0.165≤ma≤1.468GeV/c2