Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Usage patterns of collaborative tagging systems
Journal of Information Science
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
A statistical comparison of tag and query logs
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
What do people ask their social networks, and why?: a survey study of status message q&a behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Conversational tagging in twitter
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Improving social bookmark search using personalised latent variable language models
Proceedings of the fourth ACM international conference on Web search and data mining
Technically Speaking: All A-Twitter
IEEE Spectrum
| Hi-index | 0.00 |
The free-form tags available from social bookmarking sites such as Delicious have been shown to be useful for a number of purposes and could serve as a cheap source of metadata about URLs on the web. Unfortunately recent years have seen a reduction in the popularity of such sites, however at the same time microblogging sites such as Twitter have exploded in popularity. On these sites users submit short messages (or "tweets") about what they are currently reading, thinking and doing and often post URLs. In this work we look into the similarity between top tags drawn from Delicious and high-frequency terms from tweets to ascertain whether Twitter data could serve as a useful replacement for Delicious. We investigate how these terms compare with web page content, whether or not top Twitter terms converge and determine if the terms are mostly descriptive (and therefore useful) or if they are mostly expressing sentiment or emotion. We discover that provided a large number of tweets are available referring to a chosen URL then the top terms drawn from these tweets are similar to Delicious tags and could therefore be used for similar purposes.