TY  - JOUR
AU  - Golder, Scott A.
AU  - Huberman, Bernardo A.
T1  - Usage patterns of collaborative tagging systems
JO  - Journal of Information Science
PY  - 2006/
VL  - 32
IS  - 2
SP  - 198
EP  - 208
UR  - http://jis.sagepub.com/cgi/content/abstract/32/2/198
DO  - 10.1177/0165551506062337
KW  - tagging
KW  - ol_web2.0
KW  - collaborative
KW  - social
KW  - pattern
KW  - folksonomy
KW  - usage
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
ER  -

TY  - JOUR
AU  - Golder, Scott
AU  - Huberman, Bernardo A.
T1  - The Structure of Collaborative Tagging Systems
JO  - Journal of Information Sciences
PY  - 2006/04
VL  - 32
IS  - 2
SP  - 198
EP  - 208
UR  - http://.hpl.hp.com/research/idl/papers/tags/index.html
DO  - 
KW  - background
KW  - tagging
KW  - ol_web2.0
KW  - social_software
KW  - diploma_thesis
KW  - folksonomy
KW  - folksonomy_background
KW  - emergentsemantics_evidence
L1  - golder06-structure.pdf
SN  - 
N1  - 
N1  - 
AB  - Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
ER  -

TY  - GEN
AU  - Asur, Sitaram
AU  - Huberman, Bernardo A.
A2  - 
T1  - Predicting the Future with Social Media
JO  - 
PB  - 
C1  - 
PY  - 2010/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/1003.5699
DO  - 
KW  - social_media
KW  - ol_web2.0
KW  - data_twitter
KW  - huberman
KW  - widely_related
KW  - prediction
L1  - 
N1  - Predicting the Future with Social Media
N1  - 
AB  - In recent years, social media has become ubiquitous and important for socialnetworking and content sharing. And yet, the content that is generated fromthese websites remains largely untapped. In this paper, we demonstrate howsocial media content can be used to predict real-world outcomes. In particular,we use the chatter from Twitter.com to forecast box-office revenues for movies.We show that a simple model built from the rate at which tweets are createdabout particular topics can outperform market-based predictors. We furtherdemonstrate how sentiments extracted from Twitter can be further utilized toimprove the forecasting power of social media.
ER  -