Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Usage patterns of collaborative tagging systems
Journal of Information Science
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Combating spam in tagging systems: An evaluation
ACM Transactions on the Web (TWEB)
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Tag recommendations in social bookmarking systems
AI Communications
Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Real-time visualization of network behaviors for situational awareness
Proceedings of the Seventh International Symposium on Visualization for Cyber Security
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Transient crowd discovery on the real-time social web
Proceedings of the fourth ACM international conference on Web search and data mining
Improving tag recommendation using social networks
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Tag recommendation based on Bayesian principle
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Proceedings of the 20th international conference on World wide web
A comparison of content-based tag recommendations in folksonomy systems
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Detecting collective attention spam
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Learning to annotate tweets with crowd wisdom
Proceedings of the 22nd international conference on World Wide Web companion
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The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via user-driven linking, in which users annotate their own status updates with lightweight semantic annotations -- or hashtags. Unfortunately, there is evidence that hashtag growth is not keeping pace with the growth of the overall real-time web. In a random sample of 3 million tweets, we find that only 10.2% contain at least one hashtag. Hence, in this paper we explore the possibility of predicting hashtags for un-annotated status updates. Toward this end, we propose and evaluate a graph-based prediction framework. Three of the unique features of the approach are: (i) a path aggregation technique for scoring the closeness of terms and hashtags in the graph; (ii) pivot term selection, for identifying high value terms in status updates; and (iii) a dynamic sliding window for recommending hashtags reflecting the current status of the real-time web. Experimentally we find encouraging results in comparison with Bayesian and data mining-based approaches.