Abstract
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.
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References
Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: WSDM 2008, pp. 183–194 (2008)
Baeza-Yates, R., Calderón-Benavides, L., González-Caro, C.N.: The Intention Behind Web Queries. In: Crestani, F., Ferragina, P., Sanderson, M. (eds.) SPIRE 2006. LNCS, vol. 4209, pp. 98–109. Springer, Heidelberg (2006)
Beitzel, S.M., Jensen, E.C., Frieder, O., Lewis, D.D., Chowdhury, A., Kolcz, A.: Improving automatic query classification via semi-supervised learning. In: ICDM 2005, pp. 42–49 (2005)
Broder, A.Z., Fontoura, M., Gabrilovich, E., Joshi, A., Josifovski, V., Zhang, T.: Robust classification of rare queries using web knowledge. In: SIGIR 2007, pp. 231–238 (2007)
Diemert, E., Vandelle, G.: Unsupervised query categorization using automatically-built concept graphs. In: WWW 2009, pp. 461–461 (2009)
Dong, A., Zhang, R., Kolari, P., Bai, J., Diaz, F., Chang, Y., Zheng, Z., Zha, H.: Time is of the essence: improving recency ranking using twitter data. In: WWW 2010, pp. 331–340 (2010)
Hu, J., Wang, G., Lochovsky, F., Tao Sun, J., Chen, Z.: Understanding user’s query intent with Wikipedia. In: WWW 2009, pp. 471–480 (2009)
Kudo, T., Yamamoto, K., Matsumoto, Y.: Applying conditional random fields to Japanese morphological analysis. In: EMNLP 2004, pp. 230–237 (2004)
Kulkarni, A., Teevan, J., Svore, K.M., Dumais, S.T.: Understanding temporal query dynamics. In: WSDM 2011, pp. 167–176 (2011)
Li, X., Wang, Y.-Y., Acero, A.: Learning query intent from regularized click graphs. In: SIGIR 2008, pp. 339–346 (2008)
Li, Y., Zheng, Z., Dai, H.K.: KDD CUP-2005 report: facing a great challenge. SIGKDD Explor. Newsl. 7(2), 91–99 (2005)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: WWW 2010, pp. 851–860 (2010)
Shen, D., Sun, J.-T., Yang, Q., Chen, Z.: Building bridges for web query classification. In: SIGIR 2006, pp. 131–138 (2006)
Talukdar, P., Crammer, K.: New Regularized Algorithms for Transductive Learning. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 442–457. Springer, Heidelberg (2009)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML 2003, pp. 912–919 (2003)
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Yoshida, M., Arase, Y. (2012). Exploiting Twitter for Spiking Query Classification. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_12
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DOI: https://doi.org/10.1007/978-3-642-35341-3_12
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