COMBATING SOCIAL BOOKMARKING POLLUTION

Abstract While most social computing platforms like blogs and photo/video sharing sites are designed to upload and share contents generated by users themselves, social bookmarking services (SBM) is a tool to store website information for further reference. The information itself is not newly generated but rather scooped off the web by users. In this paper, we provide a basic analysis of how users in fact utilize SBM.

2007

Social bookmarking services have become recently popular in the Web. Along with the rapid increase in the amount of social bookmarks, future applications could leverage this data for enhancing search in the Web. This paper investigates the possibility and potential benefits of a hybrid page ranking approach that would combine the ranking criteria of PageRank with the one based on social bookmarks in order to improve the search in the Web. We demonstrate and discuss the results of analytical study made in order to compare both popularity estimates. In addition, we propose a simple hybrid search method that combines both ranking metrics and we show some preliminary experiments using this approach. We hope that this study will shed new light on the character of data in social bookmarking systems and foster development of new, effective search applications for the Web.

Proceedings of the international conference on Web search and web data mining - WSDM '08, 2008

Page 1. Can Social Bookmarking Improve Web Search? ... Our dataset represents about forty million bookmarks from the social bookmarking site del.icio.us. ...

2008

In recent years, there has been tremendous growth in shared bookmarking applications. Introduced in 2003, the del. icio. us social bookmark website was one of the first of this kind of application, and has enjoyed an early and large base of committed users. A flurry of similar offerings has since been unveiled [see (Hammond, et al., 2005) for a recent review].

2009

With web contents being generated and shared at an ever-increasing pace, a number of approaches to effectively control and retrieve contents have been developed. Social tagging is a widely implemented method for classifying contents resulting from the dispersed activities of users. Social bookmarking services (SBM) is a web service with the purpose of making information generally available on a shared basis. Accumulation of tags on SBM occurs mainly without inviting the collaboration of others, but on the basis of ...

Proceedings of the 7th ACM/IEEE-CS joint conference on …

Social bookmarking is an emerging type of a Web service that helps users share, classify, and discover interesting resources. In this paper, we explore the concept of an enhanced search, in which data from social bookmarking systems is exploited for enhancing search in the Web. We propose combining the widely used link-based ranking metric with the one derived using social bookmarking data. First, this increases the precision of a standard link-based search by incorporating popularity estimates from aggregated data of bookmarking users. Second, it provides an opportunity for extending the search capabilities of existing search engines. Individual contributions of bookmarking users as well as the general statistics of their activities are used here for a new kind of a complex search where contextual, temporal or sentiment-related information is used. We investigate the usefulness of social bookmarking systems for the purpose of enhancing Web search through a series of experiments done on datasets obtained from social bookmarking systems. Next, we show the prototype system that implements the proposed approach and we present some preliminary results.

2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009

Web 2.0 applications innovate traditional informative services providing Web users with a set of tools for publishing and sharing information. Social bookmarking systems are an interesting example of this trend where users generate new contents. Unfortunately, the growing amount of available resources makes hard the task of accessing to relevant information in these environments. Recommender systems face this problem filtering relevant resources connected to users' interests and preferences. In particular, collaborative filtering recommender systems produce suggestions using opinions of similar users, the neighbors. The task of finding the neighbors is difficult in environment such as social bookmarking systems, since bookmarked resources belong to variegated domains. In this paper we propose a methodology for partitioning users, tags and resources into domains of interest. Filtering tags and resources in accordance to the specific domains we can select a different set of neighbors for each domain, improving the accuracy of recommendations.

2012 Third International Conference on Networking and Computing, 2012

There are enormous amount of pages on web. We consider that there are many web pages periodically viewed by users. For example, we can imagine that pages about the illustration of New Year's cards are generally viewed near the end of each year. In this paper, we investigate the property of such periodically viewed web pages using social bookmarking systems. Specifically, we will confirm that how many pages are actually bookmarked with a specific period and why they are bookmarked periodically. We focus on the frequency analysis with a discrete Fourier transform as a concrete tool to analyze the temporal periodicity of bookmarks, and we use the Hatena Bookmark dataset as a concrete dataset of social bookmarks. As a result, we detected 1385 periodically bookmarked pages which accounts for approximately 1% of the pages stored in the dataset.

2009

Group '09 : proceedings of the 2009 ACM SIGCHI international conference on supporting group work