Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews, and more important, what kind of reviews do such interactions induce? We study these questions using data from epinions.com, one of the largest product review websites where users can subscribe to one another. By applying both panel data and flexible matching methods, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings systematically change, and become more negative and more varied. Such tradeoff has not been previously documented, and has important implications for not just product review websites, but user-generated content sites as well.

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Collaborative tagging systems are now popular tools for organising and sharing information on the Web. While collaborative tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag.

News articles often contain information about the future. Given the huge volume of information available nowadays, an automatic way for extracting and summarizing futurerelated information is desirable. Such information will allow people to obtain a collective image of the future, to recognize possible future scenarios and be prepared for the future events. We propose a model-based clustering algorithm for detecting future events based on information extracted from a text corpus. The algorithm takes into account both textual and temporal similarity of sentences. We demonstrate that our algorithm can be used to discover future events and estimate their probabilities over time.

In this paper we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Secondly, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance much better than the original HITS algorithm and simple statistical measures currently used in most collaborative tagging systems.