Abstract
Diversifying the results retieved is an effective approach to tackling users’ information needs in Twitter, which typically described by query phrase are often ambiguous and have more than one interpretation. Due to tweets being often very short and lacking in reliable grammatical sytle, it reduces the effectiveness of traditional IR and NLP techniques. However, Twitter, as a social media, also presents interesting opportunies for this task (for example the author information such as the number of statuses). In this paper, we firstly address diversitication of the search results in Twitter with a learning method and explore a series of diversity features describing the relationship between tweets which include tweet content, sub-topic of tweet and the Twitter specific social information such as hashtags. The experimental results on the Tweets2013 datasets demonstrate the effectiveness of the learning approach. Additionally, the Twitter retrieval task achieves improvement by taking into account the diversity features. Finally, we find the sub-topic and Twitter specific social features can help solve the diversity task, especially the post time, hashtags of tweet and the location of author.
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Tweets come from Tweets2013 corpus.
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Tweets come from Tweets2013 corpus.
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The software can be downloaded from http://mloss.org/software/view/527/.
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The corpus is publicly available at http://wis.ewi.tudelft.nl/airs2013.
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Tweets come from Tweets2013 corpus.
- 7.
Tweets come from Tweets2013 corpus.
- 8.
Tweets come from Tweets2013 corpus.
References
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: an analysis of a microblogging community. In: Zhang, H., Spiliopoulou, M., Mobasher, B., Giles, C.L., McCallum, A., Nasraoui, O., Srivastava, J., Yen, J. (eds.) WebKDD 2007. LNCS, vol. 5439, pp. 118–138. Springer, Heidelberg (2009)
Busch, M., Gade, K., Larson, B., Lok, P., Luckenbill, S., Lin, J.: Earlybird: Real-time search at twitter. In: IEEE 28th International Conference on Data Engineering (ICDE 2012), Washington, DC, USA (Arlington, Virginia), 1–5 April 2012, pp. 1360–1369 (2012)
Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 5–14. ACM (2009)
Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, August 24–28 1998, Melbourne, Australia, pp. 335–336 (1998)
Santos, R.L.T., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26–30 April 2010, pp. 881–890 (2010)
Zhu, Y., Lan, Y., Guo, J., Cheng, X., Niu, S.: Learning for search result diversification. In: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, Gold Coast, QLD, Australia, 06–11 July 2014, pp. 293–302 (2014)
Teevan, J., Ramage, D., Morris, M.R.: #Twittersearch: a comparison of microblog search and web search. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, 9–12 February 2011, pp. 35–44 (2011)
Ozsoy, M.G., Onal, K.D., Altingovde, I.S.: Result diversification for tweet search. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014, Part II. LNCS, vol. 8787, pp. 78–89. Springer, Heidelberg (2014)
Jabeur, L.B., Tamine, L., Boughanem, M.: Uprising microblogs: a bayesian network retrieval model for tweet search. In: Proceedings of the ACM Symposium on Applied Computing, SAC 2012, Riva, Trento, Italy, 26–30 March 2012, pp. 943–948 (2012)
Zhang, X., He, B., Luo, T., Li, B.: Query-biased learning to rank for real-time twitter search. In: 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, USA, 29 October–02 November 2012, pp. 1915–1919 (2012)
Luo, Z., Osborne, M., Petrovic, S., Wang, T.: Improving twitter retrieval by exploiting structural information. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 22–26 July 2012, Toronto, Ontario, Canada (2012)
Luo, Z., Osborne, M., Wang, T.: Opinion retrieval in twitter. In: Proceedings of the Sixth International Conference on Weblogs and Social Media, Dublin, Ireland, 4–7 June 2012 (2012)
Carterette, B., Chandar, P.: Probabilistic models of ranking novel documents for faceted topic retrieval. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, 2–6 November 2009, pp. 1287–1296 (2009)
Radlinski, F., Dumais, S.T.: Improving personalized web search using result diversification. In: SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, 6–11 August 2006, pp. 691–692 (2006)
He, J., Hollink, V., de Vries, A.P.: Combining implicit and explicit topic representations for result diversification. In: The 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, Portland, OR, USA, 12–16 August 2012, pp. 851–860 (2012)
Tao, K., Hauff, C., Houben, G.-J.: Building a microblog corpus for search result diversification. In: Banchs, R.E., Silvestri, F., Liu, T.-Y., Zhang, M., Gao, S., Lang, J. (eds.) AIRS 2013. LNCS, vol. 8281, pp. 251–262. Springer, Heidelberg (2013)
Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, Singapore, 20–24 July 2008, pp. 659–666 (2008)
Zhai, C., Lafferty, J.D.: A risk minimization framework for information retrieval. Inf. Process. Manage. 42(1), 31–55 (2006)
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Wang, Y., Luo, Z., Yu, Y. (2016). Learning for Search Results Diversification in Twitter. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_20
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DOI: https://doi.org/10.1007/978-3-319-39958-4_20
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