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  1. IEEE International Workshop on Business Applications of Social Network Analysis (BASNA).
  2. 2010 IEEE International Workshop on: Business Applications of Social Network Analysis (BASNA)
  3. How social is social bookmarking?
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2010 IEEE International Workshop on: Business Applications of Social Network Analysis (BASNA)
Randomized tag recommendation in social networks and classification of spam posts
Ad amīcus: Analysis of click behaviour in social relationships extracted from URLs
Agile Knowledge Management System: Leveraging intra organizational Social Networking platforms for augmenting Knowledge Management system
Mapping communities in large virtual social networks: Using Twitter data to find the Indie Mac community
Community detection via semi-synchronous label propagation algorithms
Community discovery in a growing model of social networks
The sneeze algorithm: A social network & biomimetic approach for intrusion detection in wireless networks
A proposal to enhance cross-marketing via social networks by modelling and analyzing market basket like patterns across social network groups
How social is social bookmarking?
Digging social networks by mashups to support recruitment and selection functions in university student intake process
Business intelligence from Twitter for the television media: A case study
Identifying key players in a social network: Measuring the extent of an individual's Neighbourhood Connectivity
Discovering unique members from mobile social networks
A quantitative methodology to identify relevant users in social networks
Trust-based Dynamic Web service Composition using Social Network Analysis

How social is social bookmarking?

Content Provider IEEE Xplore Digital Library
Author Ram, S. Wei Wei
Copyright Year 2010
Description Author affiliation: Department of Management Information Systems, University of Arizona, Tucson AZ 85721 (Ram, S.; Wei Wei)
Abstract Social bookmarking services allow a user to make her personal collection of favorite web resources accessible by the public. The content of this collection can attract users of “similar minds” and therefore has tremendous potential to enable networking and collaboration. In this research, we analyzed a large dataset collected from one of the most popular social bookmarking services. To understand why there is a large gap between a user's explicit network and her implicit user-user association networks based on common resources or common tags, we compared a users' bookmark resources and tags to those of her explicit network members. Our results suggest that a typical social bookmarking service user does not create her explicit network based on common interests. We discuss the implications behind the gap between a user's explicit network and implicit network and propose solutions to enhance and improve the “social” functions of social bookmarking services.
Starting Page 1
Ending Page 8
File Size 842608
Page Count 8
File Format PDF
ISBN 9781424489992
e-ISBN 9781424490004
DOI 10.1109/BASNA.2010.5730302
Language English
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date 2010-12-15
Publisher Place India
Access Restriction Subscribed
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword Fans Social network services Image edge detection Communities Clustering algorithms Collaboration Tagging Resource Social network Tag Social bookmarking Delicious
Content Type Text
Resource Type Article
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