Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to

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. 1

On-line forums for the collaborative creation of bodies of information are a phenomenon of rising importance; the Wikipedia is one of the best-known examples. The open nature of such forums could benefit from a notion of reputation for its authors. Author reputation could be used to flag new contributions from low-reputation authors, and it could be used to allow only authors with good reputation to contribute to controversial or critical pages. A reputation system for the Wikipedia would also provide an incentive to give high-quality contributions. We present in this paper a novel type of contentdriven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits and text additions they perform to Wikipedia articles are longlived, and they lose reputation when their changes are undone in short order. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691,551 pages and 5,587,523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, and of being undone.

Reputation systems have been popular in estimating the trustworthiness and predicting the future behavior of nodes in a large-scale distributed system where nodes may transact with one another without prior knowledge or experience. One of the fundamental challenges in distributed reputation management is to understand vulnerabilities and develop mechanisms that can minimize the potential damages to a system by malicious nodes. In this paper, we identify three vulnerabilities that are detrimental to decentralized reputation management and propose TrustGuard -- safeguard framework for providing a highly dependable and yet efficient reputation system. First, we provide a dependable trust model and a set of formal methods to handle strategic malicious nodes that continuously change their behavior to gain unfair advantages in the system. Second, a transaction based reputation system must cope with the vulnerability that malicious nodes may misuse the system by flooding feedbacks with fake transactions. Third, but not least, we identify the importance of filtering out dishonest feedbacks when computing reputation-based trust of a node, including the feedbacks filed by malicious nodes through collusion. Our experiments show that, comparing with existing reputation systems, our framework is highly dependable and effective in countering malicious nodes regarding strategic oscillating behavior, flooding malevolent feedbacks with fake transactions, and dishonest feedbacks.

Traditional centralised approaches to security are difficult to apply to large, distributed marketplaces in which software agents operate. Developing a notion of trust that is based on the reputation of agents can provide a softer notion of security that is sufficient for many multi-agent applications. In this paper, we address the issue of incentivecompatibility (i.e. how to make it optimal for agents to share reputation information truthfully), by introducing a sidepayment scheme, organised through a set of broker agents, that makes it rational for software agents to truthfully share the reputation information they have acquired in their past experience. We also show how to use a cryptographic mechanism to protect the integrity of reputation information and to achieve a tight bounding between the identity and reputation of an agent. 1.

Abstract. Interactions between entities unknown to each other are inevitable in the ambient intelligence vision of service access anytime, anywhere. Trust management through a reputation mechanism to facilitate such interactions is recognized as a vital part of mobile ad hoc networks, which features lack of infrastructure, autonomy, mobility and resource scarcity of composing light-weight terminals. However, the design of a reputation mechanism is faced by challenges of how to enforce reputation information sharing and honest recommendation elicitation. In this paper, we present a reputation model, which incorporates two essential dimensions, time and context, along with mechanisms supporting reputation formation, evolution and propagation. By introducing the notion of recommendation reputation, our reputation mechanism shows effectiveness in distinguishing truth-telling and lying agents, obtaining true reputation of an agent, and ensuring reliability against attacks of defame and collusion. 1

Reputation systems provide mechanisms to produce a metric encapsulating reputation for a given domain for each identity within the system. These systems seek to generate an accurate assessment in the face of various factors including but not limited to unprecedented community size and potentially adversarial environments. We focus on attacks and defense mechanisms in reputation systems. We present an analysis framework that allows for general decomposition of existing reputation systems. We classify attacks against reputation systems by identifying which system components and design choices are the target of attacks. We survey defense mechanisms employed by existing reputation systems. Finally, we analyze several landmark systems in the peer-to-peer domain, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) which design components of reputation systems are most vulnerable, 2) what are the most appropriate defense mechanisms and 3) how these defense mechanisms can be integrated into existing or future reputation systems to make them resilient to attacks.

This paper offers a systematic exploration of reputation mechanism design in trading environments with opportunistic sellers of commonly known cost and ability parameters, imperfect monitoring of a seller’s actions, and two possible seller effort levels, one of which has no value to buyers. The objective of reputation mechanisms in such pure moral hazard settings is to induce sellers to exert high effort as often as possible. I study the impact of various mechanism parameters (such as the granularity of solicited feedback, the format of the public reputation profile, the policy regarding missing feedback, and the rules for admitting new sellers) on the resulting market efficiency. I find that maximum efficiency is bounded away from the hypothetical firstbest case where sellers can credibly precommit to full cooperation by a factor that is related to the probability that cooperating sellers may receive “unfair ” bad ratings. Furthermore, maximum efficiency is independent of the length of past history summarized in a seller’s public reputation profile. I apply my framework to a simplified model of eBay’s feedback mechanism and conclude that, in pure moral hazard settings, eBay’s simple mechanism is capable of inducing the maximum theoretical efficiency independently of the number of recent ratings that are being summarized in a seller’s profile. I derive optimal policies for dealing with missing feedback and easy online identity changes. Finally, I show that if the number of buyers is large, the results obtained in the monopoly case are also approximately valid in settings where multiple sellers of different reputations simultaneously offer auctions for identical goods. Key words: reputation mechanisms; moral hazard; online auctions; eBay

In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions.We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment.We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19% of microblogs contain mention of a brand. Of the branding microblogs, nearly 20 % contained some expression of brand sentiments. Of these, more than 50 % were positive and 33 % were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy.

Peer-to-peer systems, multi-agent systems, and ad hoc networks aim at exploiting synergies that result from cooperation. Yet, these systems are composed of autonomous entities that are free to decide whether to cooperate or not. Hence, incentives are indispensable to induce cooperation between autonomous entities. In this paper, we introduce incentive patterns as a means of systematically conceiving incentive schemes with respect to the specifics of the application environment. Based on economic incentive patterns, we derive several incentive patterns and discuss them with respect to a set of general characteristics. Consequently, we propose a taxonomy that classifies the derived incentive patterns.