The Effect of Social Proof on Tag Selection in Social Bookmarking Applications

The growing popularity of social bookmaking applications like flickr and del.icio.us present new challenges to system designers because the effects of social psychological factors on users' tag choices have not been examined. The social psychological principle of social proof is particularly applicable to social bookmarking because it predicts that the tags applied by users will be more similar to each other if they are provided with a list of suggested tags. This study examines the effect of social proof on tag selection by comparing the degree of similarity between tags provided by a sample group and a collection of suggested tags provided to the treatment group. The results indicate that social proof can have an effect on users' tag selection. The conclusion briefly examines the beneficial effect of social proof on the quality of social bookmarking applications and other collaborative tagging applications

Distributed bookmark sharing primitives

Ankara : The Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent Univ., 1999.Thesis (Master's) -- Bilkent University, 1999.Includes bibliographical references leaves 73-[74].İnce, KürşatM.S

Identifying experts and authoritative documents in social bookmarking systems

Social bookmarking systems allow people to create pointers to Web resources in a shared, Web-based environment. These services allow users to add free-text labels, or “tags”, to their bookmarks as a way to organize resources for later recall. Ease-of-use, low cognitive barriers, and a lack of controlled vocabulary have allowed social bookmaking systems to grow exponentially over time. However, these same characteristics also raise concerns. Tags lack the formality of traditional classificatory metadata and suffer from the same vocabulary problems as full-text search engines. It is unclear how many valuable resources are untagged or tagged with noisy, irrelevant tags. With few restrictions to entry, annotation spamming adds noise to public social bookmarking systems. Furthermore, many algorithms for discovering semantic relations among tags do not scale to the Web. Recognizing these problems, we develop a novel graph-based Expert and Authoritative Resource Location (EARL) algorithm to find the most authoritative documents and expert users on a given topic in a social bookmarking system. In EARL’s first phase, we reduce noise in a Delicious dataset by isolating a smaller sub-network of “candidate experts”, users whose tagging behavior shows potential domain and classification expertise. In the second phase, a HITS-based graph analysis is performed on the candidate experts’ data to rank the top experts and authoritative documents by topic. To identify topics of interest in Delicious, we develop a distributed method to find subsets of frequently co-occurring tags shared by many candidate experts. We evaluated EARL’s ability to locate authoritative resources and domain experts in Delicious by conducting two independent experiments. The first experiment relies on human judges’ n-point scale ratings of resources suggested by three graph-based algorithms and Google. The second experiment evaluated the proposed approach’s ability to identify classification expertise through human judges’ n-point scale ratings of classification terms versus expert-generated data

A Framework for Supporting User-Centric Collaborative Information Seeking

Collaboration is often required or encouraged for activities that are too complex or difficult to deal with for an individual. Many situations involving information seeking also call for people working together. Despite its natural appeal and situational necessity, collaboration in information seeking is an understudied domain. The nature of the available information and its role in our lives have changed significantly, but the methods and tools that are used to access and share that information in collaboration have remained largely unaltered. This dissertation is an attempt to develop a new framework for collaborative information seeking (CIS) with a focus on user-centric system designs. To develop this framework, existing practices for doing collaboration, along with motivations and methods, are studied. This initial investigation and a review of literature are followed by a series of carefully created design studies, helping us develop a prototype CIS system, Coagmento. This system is then used for a large scale laboratory experiment with a focus on studying the role and the impact of awareness in CIS projects. Through this study, it is shown that appropriate support for group awareness can help collaborators be more productive, engaged, and aware in collaboration without burdening them with additional load. Using the lessons derived from the literature as well as the set of studies presented in this dissertation, a novel framework for CIS is proposed. Such a framework could help us develop, study, and evaluate CIS systems with a more comprehensive understanding of various CIS processes, and the users of these systems.Doctor of Philosoph

Network analysis of shared interests represented by social bookmarking behaviors

Social bookmarking is a new phenomenon characterized by a number of features including active user participation, open and collective discovery of resources, and user-generated metadata. Among others, this study pays particular attention to its nature of being at the intersection of personal information space and social information space. While users of a social bookmarking site create and maintain their own bookmark collections, the users' personal information spaces, in aggregate, build up the information space of the site as a whole. The overall goal of this study is to understand how social information space may emerge when personal information spaces of users intersect and overlap with shared interests. The main purpose of the study is two-fold: first, to see whether and how we can identify shared interest space(s) within the general information space of a social bookmarking site; and second, to evaluate the applicability of social network analysis to this end. Delicious.com, one of the most successful instances of social bookmarking, was chosen as the case. The study was carried out in three phases asking separate yet interrelated questions concerning the overall level of interest overlap, the structural patterns in the network of users connected by shared interests, and the communities of interest within the network. The results indicate that, while individual users of delicious.com have a broad range of diverse interests, there is a considerable level of overlap and commonality, providing a ground for creating implicit networks of users with shared interests. The networks constructed based on common bookmarks revealed intriguing structural patterns commonly found in well-established social systems, including a core periphery structure with a high level of connectivity, which form a basis for efficient information sharing and knowledge transfer. Furthermore, an exploratory analysis of the network communities showed that each community has a distinct theme defining the shared interests of its members, at a high level of coherence. Overall, the results suggest that networks of people with shared interests can be induced from their social bookmarking behaviors and such networks can provide a venue for investigating social mechanisms of information sharing in this new information environment. Future research can be built upon the methods and findings of this study to further explore the implication of the emergent and implicit network of shared interests