著者

Yoshida Mitsuo Arase Yuki

出版者

Springer Berlin Heidelberg

雑誌

Information Retrieval Technology (ISSN:03029743)

巻号頁・発行日

vol.7675, pp.138-149, 2012-12

被引用文献数

1

---

We propose a method for classifying queries whose frequency spikesin a search engine into their topical categories such as celebrities and sports. Unlikeprevious methods using Web search results and query logs that take a certainperiod of time to follow spiking queries, we exploit Twitter to timely classifyspiking queries by focusing on its massive amount of super-fresh content. Theproposed method leverages unique information in Twitter—not only tweets butalso users and hashtags. We integrate such heterogeneous information in a graphand classify queries using a graph-based semi-supervised classification method.We design an experiment to replicate a situation when queries spike. The resultsindicate that the proposed method functions effectively and also demonstrate thataccuracy improves by combining the heterogeneous information in Twitter.