Clustering Algorithms
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Hierarchical, perceptron-like learning for ontology-based information extraction
Proceedings of the 16th international conference on World Wide Web
Combining a Logical and a Numerical Method for Data Reconciliation
Journal on Data Semantics XII
Tweet the debates: understanding community annotation of uncollected sources
WSM '09 Proceedings of the first SIGMM workshop on Social media
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Proceedings of the 6th International Conference on Semantic Systems
Twarql: tapping into the wisdom of the crowd
Proceedings of the 6th International Conference on Semantic Systems
Towards semantically-interlinked online communities
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Exploring the wisdom of the tweets: towards knowledge acquisition from social awareness streams
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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Microblogging platforms, such as Twitter, now provide web users with an on-demand service to share and consume fragments of information. Such fragments often refer to real-world events e.g., shows, conferences and often refer to a particular event component such as a particular talk, providing a bridge between the real and virtual worlds. The utility of tweets allows companies and organisations to quickly gauge feedback about their services, and provides event organisers with information describing how participants feel about their event. However, the scale of the Web, and the sheer number of Tweets which are published on an hourly basis, makes manually identifying event tweets difficult. In this paper we present an automated approach to align tweets with the events which they refer to. We aim to provide alignments on the sub-event level of granularity. We test two different machine learning-based techniques: proximity-based clustering and classification using Naive Bayes. We evaluate the performance of our approach using a dataset of tweets collected from the Extended Semantic Web Conference 2010. The best F0.2 scores obtained in our experiments for proximity-based clustering and Naive Bayes were 0.544 and 0.728 respectively.