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64
Opinion Mining and Sentiment Analysis
, 2008
"... An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, active ..."
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Cited by 749 (3 self)
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An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include materialon summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
The digitization of word of mouth: Promise and challenges of online feedback mechanisms
- Management Science
, 2003
"... Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a ..."
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Cited by 206 (5 self)
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Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for operations research/management science (OR/MS) research.
The Value of Reputation on eBay: A Controlled Experiment
- Experimental Economics
, 2003
"... The latest version of this working paper can be found at ..."
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Cited by 177 (9 self)
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The latest version of this working paper can be found at
Reputation mechanisms
- Handbook on Economics and Information Systems
, 2006
"... Reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to hav ..."
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Cited by 46 (2 self)
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Reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based reputation mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, marketing, and psychology in order to take into consid-eration the special properties of online environments. Finally, it identifies opportunities that this new area presents for information systems research. 1
Visualizing online auctions
- Journal of Computational and Graphical Statistics
, 2005
"... Online auctions have been the subject of many empirical research efforts in the fields of economics and information systems. These research efforts are often based on analyzing data from websites such as eBay.com which provide public information about sequences of bids in closed auctions, typ-ically ..."
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Cited by 36 (17 self)
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Online auctions have been the subject of many empirical research efforts in the fields of economics and information systems. These research efforts are often based on analyzing data from websites such as eBay.com which provide public information about sequences of bids in closed auctions, typ-ically in the form of tables on HTML pages. The existing literature on online auctions focuses on tools like summary statistics and more formal statistical methods such as regression models. How-ever, there is a clear void in this growing body of literature in developing appropriate visualization tools. This is quite surprising, given that the sheer amount of data that can be found on sites such as eBay.com is overwhelming and can often not be displayed informatively using standard statistical graphics. In this paper we introduce graphical methods for visualizing online auction data in ways that are informative and relevant to the types of research questions that are of interest. We start by using profile plots that reveal aspects of an auction such as bid values, bidding intensity, and bidder strategies. We then introduce the concept of statistical zooming (STAT-zoom) which can scale up to be used for visualizing large amounts of auctions. STAT-zoom adds the capability of looking at data summaries at various time scales interactively. Finally, we develop auction calen-dars and auction scene visualizations for viewing a set of many concurrent auctions. The different visualization methods are demonstrated using data on multiple auctions collected from eBay.com.
Defending Online Reputation Systems against Collaborative Unfair Raters through Signal Modeling and Trust
"... Online feedback-based rating systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of honest ratings is relatively small and unfair ratings can contri ..."
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Cited by 14 (3 self)
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Online feedback-based rating systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of honest ratings is relatively small and unfair ratings can contribute to a significant portion of the overall ratings. In addition, the lack of unfair rating data from real human users is another obstacle toward realistic evaluation of defense mechanisms. In this paper, we propose a set of methods that jointly detect smart and collaborative unfair ratings based on signal modeling. Based on the detection, a framework of trust-assisted rating aggregation system is developed. Furthermore, we design and launch a Rating Challenge to collect unfair rating data from real human users. The proposed system is evaluated through simulations as well as experiments using real attack data. Compared with existing schemes, the proposed system can significantly reduce the impact from collaborative unfair ratings.
2004): “Effects of Reputation Mechanisms on Fraud Prevention in eBay Auctions
"... This paper shows empirically that eBay’s feedback system doc-uments successful transactions, but often fails to inform users of unsuccessful ones. A data set containing information from 3776 auctions I collected from eBay’s platform, gives reason to assume that the feedback system exhibits a positiv ..."
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Cited by 14 (0 self)
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This paper shows empirically that eBay’s feedback system doc-uments successful transactions, but often fails to inform users of unsuccessful ones. A data set containing information from 3776 auctions I collected from eBay’s platform, gives reason to assume that the feedback system exhibits a positive bias due to the na-ture of mutual feedback that is made public instantly. Sellers and buyers are able to hold feedback hostages by refusing to leave feed-back until the opposite party has provided a report. In cases of problematic or fraudulent transactions, this behavior may lead to false feedback reports, or no feedback-provision altogether for fear of retaliation. In such cases, important information to the com-munity about transactions ’ efficiency is lost. In a simple model I rationalize false feedback reports, after which I suggest a change of the current feedback mechanism that circumvents the problem of feedback-retaliation, while at the same time providing incen-tives to participate in the feedback-provision process. 1
Anomaly detection in feedback-based reputation systems through temporal and correlation analysis
- in Proc. of 2nd IEEE Int. Conf. on Social Computing
, 2010
"... Abstract—As the value of reputation systems is widely recognized, the incentive to manipulate such systems is rapidly growing. We propose TAUCA, a scheme that identifies malicious users and recovers reputation scores from a novel angle: combination of temporal analysis and user correlation analysis. ..."
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Cited by 14 (4 self)
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Abstract—As the value of reputation systems is widely recognized, the incentive to manipulate such systems is rapidly growing. We propose TAUCA, a scheme that identifies malicious users and recovers reputation scores from a novel angle: combination of temporal analysis and user correlation analysis. Benefiting from the rich information in the time-domain, TAUCA identifies the products under attack, the time when attacks occur, and malicious users who insert dishonest ratings. TAUCA and two other representative schemes are tested against real user attack data collected through a cyber competition. TAUCA demonstrates significant advantages. It largely improves the detection rate and reduces the false alarm rate in the detection of malicious users. It also effectively reduces the bias in the recovered reputation scores. I.
Self-interest, reciprocity, and participation in online reputation systems’, MIT Sloan Working Papers No
, 2004
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Revolutionary research strategies for e-business: a philosophy of science view in the age of the Internet, economics. Chapter 2 in
- R.J. Kauffman, P.P. Tallon (Eds.), Economics, Information Systems, and Electronic Commerce: Empirical Research, M. E. Sharpe, Armonk, NY
"... Just as the Internet has changed the way many businesses conduct business, so can it change the way that academic researchers design and execute research in e-business management. We present a series of re-volutionary research strategies that employ six new data-collecting methodologies that can be ..."
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Cited by 7 (3 self)
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Just as the Internet has changed the way many businesses conduct business, so can it change the way that academic researchers design and execute research in e-business management. We present a series of re-volutionary research strategies that employ six new data-collecting methodologies that can be used in conjunction with Internet technology. Data-collecting agents can gather very large amounts of data from the World Wide Web in a fraction of the time and the cost that it takes to gather data using traditional re-search methodologies. Online experiments, online judgment tasks, and online surveys expand the reach of the researcher and reduce the cost when compared to traditional experiments, judgment tasks, and sur-veys. Because of the vast amount of data available online, research designs such as massive quasi-experiments can be conducted that allow the researcher to find subjects without taking them out of their own environment who meet some pre-determined requirements, or some data that match a set of experi-mental or empirical test conditions. Finally, log files can be used to track a person‘s m ovem ents and ac-tions through a Web site. We investigate these relatively new tools from a philosophy of science perspec-tive. Using R unkel and M cG rath‘s [110] three-horned dilemma model for traditional research methodol-ogies as a basis, we develop a framework that illustrates the strengths and weaknesses of these new tools
