Exploiting Twitter for Spiking Query Classification
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.
KeywordsQuery Classification Spiking Query Twitter
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