Unsupervised and supervised learning to evaluate event relatedness based on content mining from social-media streams

Author:

Lee Chung-Hong

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference44 articles.

1. Aggarwal, C. C., Han, J., Wang, J., & Yu, P.S. (2003). A framework for clustering evolving data streams. In Proceedings of the 29th international conference on very large data bases, Berlin, Germany (Vol. 29).

2. Becker, H., Naaman, M., & Gravano, L. (2009). Event identification in social media. In Proceedings of the ACM SIGMOD workshop on the web and databases (WebDB ‘09).

3. Becker, H., Naaman, M., & Gravano, L. (2010). Learning similarity metrics for event identification in social media. In Proceedings of the 3rd ACM International Conference on Web search and data mining, New York, USA.

4. Becker, H., Chen, F., Iter, D., Naaman, M., & Gravano, L. (2011). Selecting quality twitter content for events. In Proceedings of the 25th ACM AAAI international conference on association for the advancement of artificial intelligence, San Francisco, USA.

5. Becker, H., Naaman, M., & Gravano, L. (2011a). Beyond trending topics: real-world event identification on Twitter. In Proceedings of the 25th ACM AAAI international conference on association for the advancement of artificial intelligence, San Francisco, USA.

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