• Publications
  • Influence
Usage patterns of collaborative tagging systems
TLDR
A dynamic model of collaborative tagging is presented that predicts 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. Expand
Predicting the popularity of online content
Early patterns of Digg diggs and YouTube views reflect long-term user interest.
Free Riding on Gnutella
TLDR
It is argued that free riding leads to degradation of the system performance and adds vulnerability to the system, and copyright issues might become moot compared to the possible collapse of such systems. Expand
The Structure of Collaborative Tagging Systems
TLDR
A dynamical model of collaborative tagging is presented that predicts 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. Expand
The dynamics of viral marketing
TLDR
While on average recommendations are not very effective at inducing purchases and do not spread very far, this work presents a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective. Expand
Search in Power-Law Networks
TLDR
A number of local search strategies that utilize high degree nodes in power-law graphs and that have costs scaling sublinearly with the size of the graph are introduced and demonstrated on the GNUTELLA peer-to-peer network. Expand
Predicting the Future with Social Media
TLDR
It is shown that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors and improve the forecasting power of social media. Expand
Social Networks that matter: Twitter under the Microscope
TLDR
A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers. Expand
Predicting the Future with Social Media
  • S. Asur, B. Huberman
  • Computer Science, Physics
  • IEEE/WIC/ACM International Conference on Web…
  • 29 March 2010
TLDR
It is shown that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors and improve the forecasting power of social media. Expand
Rhythms of social interaction: messaging within a massive online network
TLDR
This paper studies the social net- work service Facebook, which began in early 2004 in select universities, but grew quickly to encompass a very large number of universities. Expand
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