Asur, S. & Huberman, B. A. (2010),
'Predicting the Future with Social Media'
, cite arxiv:1003.5699
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Huberman, B. A.; Romero, D. M. & Wu, F. (2008),
'Social networks that matter: Twitter under the microscope'
, cite arxiv:0812.1045
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
Scholars, advertisers and political activists see massive online socialnetworks as a representation of social interactions that can be used to studythe propagation of ideas, social bond dynamics and viral marketing, amongothers. But the linked structures of social networks do not reveal actualinteractions among people. Scarcity of attention and the daily rythms of lifeand work makes people default to interacting with those few that matter andthat reciprocate their attention. A study of social interactions within Twitterreveals that the driver of usage is a sparse and hidden network of connectionsunderlying the declared set of friends and followers.
Golder, S. & Huberman, B. A. (2006),
'The Structure of Collaborative Tagging Systems', Journal of Information Sciences
32
(2)
, 198--208
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. A. & Huberman, B. A. (2006),
'Usage patterns of collaborative tagging systems', Journal of Information Science
32
(2)
, 198-208
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. A. & Huberman, B. A. (2006),
'Usage patterns of collaborative tagging systems', Journal of Information Science
32
(2)
, 198--208
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. A. & Huberman, B. A. (2006),
'Usage patterns of collaborative tagging systems', Journal of Information Science
32
(2)
, 198--208
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. A. & Huberman, B. A. (2006),
'Usage patterns of collaborative tagging systems', Journal of Information Science
32
(2)
, 198-208
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. & Huberman, B. A. (2005),
'The Structure of Collaborative Tagging Systems', Journal of Information Science
32
(2)
, 198--208
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. & Huberman, B. A. (2005),
'The Structure of Collaborative Tagging Systems'
, Technical report, Information Dynamics Lab, HP Labs
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
Golder, S. & Huberman, B. A. (2005),
'The Structure of Collaborative Tagging Systems'
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
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.
Golder, S. & Huberman, B. A. (2005),
'The Structure of Collaborative Tagging Systems'
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
Golder, S. & Huberman, B. A. (2005),
'The Structure of Collaborative Tagging Systems'
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
Golder, S. & Huberman, B. A. (2005),
'The Structure of Collaborative Tagging Systems'
, Technical report, Information Dynamics Lab, HP Labs
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
Tyler, J. R.; Wilkinson, D. M. & Huberman, B. A. (2003),
'Email as Spectroscopy: Automated Discovery of Community Structure within Organizations'
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
Tyler, J. R.; Wilkinson, D. M. & Huberman, B. A.
(2003), Email as Spectroscopy: Automated Discovery of Community Structure within Organizations
, Kluwer, B.V.
, Deventer, The Netherlands, The Netherlands
, pp. 81--96
.
[Volltext]
[Kurzfassung]
[BibTeX]
[Endnote]
We describe a method for the automatic identification of communities of practice from email logs within an organization. We use a betweenness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an email corpus of nearly one million messages collected over a two-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.