We propose a method for classifying queries whose frequency spikes in a search engine into their topical categories such as celebrities and sports. Unlike previous methods using Web search results and query logs that take a certain period of time to follow spiking queries, we exploit Twitter to timely classify spiking queries by focusing on its massive amount of super-fresh content. The proposed method leverages unique information in Twitter - not only tweets but also users and hashtags. We integrate such heterogeneous information in a graph and classify queries using a graph-based semi-supervised classification method. We design an experiment to replicate a situation when queries spike. The results indicate that the proposed method functions effectively and also demonstrate that accuracy improves by combining the heterogeneous information in Twitter. © Springer-Verlag 2012.
CITATION STYLE
Yoshida, M., & Arase, Y. (2012). Exploiting twitter for spiking query classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7675 LNCS, pp. 138–149). https://doi.org/10.1007/978-3-642-35341-3_12
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