Abstract
Collaborative tagging systems (CTS) offer an interesting social computing application context for topic detection and tracking research. In this paper, we apply a statistical approach for discovering topic-specific bursts from a popular CTS - del.icio.us. This approach allows trend discovery from different components of the system such as users, tags, and resources. Based on the detected topic bursts, we perform a preliminary analysis of related burst formation patterns. Our findings indicate that users and resources contributing to the bursts can be classified into two categories: old and new, based on their past usage histories. This classification scheme leads to interesting empirical findings.
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Sun, A., Zeng, D., Li, H., Zheng, X. (2008). Discovering Trends in Collaborative Tagging Systems. In: Yang, C.C., et al. Intelligence and Security Informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69304-8_37
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DOI: https://doi.org/10.1007/978-3-540-69304-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69136-5
Online ISBN: 978-3-540-69304-8
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