Champions of social classification praise its flexible and collaborative nature, in contrast to the rigidity and authoritarianism that they see in traditional classificative structures (such as Kroski, 2005; Shirky, 2005c; Merholz, 2004). In the view of these writers, social classification applications such as the photo storage Web site Flickr and the Web bookmarks manager del.icio.us are both democratic, incorporating the participation of all Web users, and emergent, changing rapidly in response to new content. On the other hand, traditional methods for organizing information, particularly those that involve hierarchy, are seen as exclusive, because they may not represent all usersâ viewpoints, and imprecise, because they cannot be easily adapted for the rapid pace of content development engendered by Web publishing. Two claims appear to underlie these descriptions of social classification. One, that the goal of classification is to identify and locate items based on a personal sense of appropriate categorization, and two, that, if enough other users index (or tag) items according to their own personal ideas of appropriate categorization, then all possibilities will be represented, and both searching and browsing will be facilitated. This paper will evaluate these claims, particularly in regards to the role and nature of authority in organizational schemes, and the intersection of authority with an organizational schemeâ s purpose. I consider these issues for three services often associated with social classification systems: * Indexing of personal collections. * Sharing of indexed personal collections. * Merging of personal collections into a group-indexed aggregate collection. The bookmarks manager del.icio.us is the primary example of a social classification system used throughout this paper.

Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona / It has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta-search and document categorization. Meta-search engines improve precision by selecting and integrating search results fromgeneric or domain-specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta-search engine that has real-time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing Meta- Spider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.

This paper uses Husserlâ s theory of phenomenology to provide a model for the relationship between user-centered tagging systems, such as del.icio.us, and the more highly structured systems of the Semantic Web. Using three aspects of phenomenological theoryâ the movement of the mind out towards an entity and then back in an act of reflection, multiplicities within unity, and the sharing of intentionalities within a communityâ the discussion suggests that both tagging systems and the Semantic Web foster an intersubjective domain for the sharing and use of information resources. The Semantic Web, however, resembles traditional library systems, in that it relies for this intersubjective domain on the conscious implementation of domain-centered standards which are then encoded for machine processing, while tagging systems work on implied principles of emergence.

Objectives: To carry out a usability evaluation of the recently launched South London and Maudsley NHS Trust library website. Methods: A variety of standard methodologies were employed; content and design evaluation of selected comparable sites; focus groups; a questionnaire survey of library and Web development staff; heuristic evaluation; observation testing; card sorting/cluster analysis, and label intuitiveness/category membership testing. All participants were staff of or providers of services to the trust. Demographic information was recorded for each participant. Results: Test participants' overall responses to the site were enthusiastic and favourable, indicating the scope and and content of the site to be broadly appropriate to the user group. Testers made numerous suggestions for new content. Usability problems were discovered in two main areas: in the organisation of the site, and in the terminology used to refer to information services and sources. Based on test results, proposals for a revised menu structure, improved accessibility, and changes to the terminology used within the site are presented. Conclusion: Usability evaluation methods, appropriately scaled, can be advantageously applied to NHS library web sites by an individual web editor working alone.

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Artificial Intelligence Lab, Department of MIS, University of Arizona / Research has shown that most usersâ online information searches are suboptimal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links and text in a search engine result set to expand user queries with a variable number of keywords in two manners. Positive expansion adds terms to a userâ s keywords with a Boolean â and,â negative expansion adds terms to the userâ s keywords with a Boolean â not.â Each algorithm was examined for three user groups, high, middle, and low achievers, who were classified according to their overall performance. The interactions of users with different levels of expertise with different expansion types or algorithms were evaluated. The genetic algorithm with negative expansion tripled recall and doubled precision for low achievers, but high achievers displayed an opposed trend and seemed to be hindered in this condition. The effect of other conditions was less substantial.

Searching for information pervades a wide spectrum of human activity, including learning and problem solving. With recent changes in the amount of information available and the variety of means of retrieval, there is even more need to understand why some searchers are more successful than others. This study was undertaken to advance our understanding of expertise in seeking information on the Web by identifying strategies and attributes that will increase the chance of a successful search on the Web. A model that illustrated the relationship between strategies and attributes and a successful search was also created. The strategies were: Evaluation, Navigation, Affect, Metacognition, Cognition, and Prior knowledge. Attributes included Age, Sex, Years of experience, Computer knowledge, and Info-seeking knowledge. Success was defined as finding a target topic within 30 minutes. Participants were from three groups. Novices were 10 undergraduate pre-service teachers who were trained in pedagogy but not specifically in information seeking. Intermediates were nine final-year master's students who had received training on how to search but typically had not put heir knowledge into extensive practice. Experts were 10 highly experienced professional librarians working in a variety of settings including government, industry, and university. Participants' verbal protocols were transcribed verbatim into a text file and coded. These codes, along with Internet temporary files, a background questionnaire, and a post-task interview were the sources of the data. Since the variable of interest was the time to finding the topic, in addition to ANOVA and Pearson correlation, survival analysis was used to explore the data. The most significant differences in patterns of search between novices and experts were found in the Cognitive, Metacognitive, and Prior Knowledge strategies. Based on the fitted survival model, Typing Keyword, Criteria to evaluate sites, and Information-Seeking Kno

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