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Measuring Social Tag Confidence: Is It a Good or Bad Tag?

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Web-Age Information Management (WAIM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6897))

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Abstract

Social tagging is an increasingly popular way to describe latent semantic information of web resources and thus is widely used to improve the performance of information retrieval system. However, there also has been significant variance of the quality of social tags because they can be annotated by folks on the web freely. As a consequence, how to measure the quality of social tags (referred to as social tag confidence) becomes an important issue. In this paper, we propose a statistic model to measure the confidence of social tags by utilizing a combination of three attributes of a social tag: web resource, tag, and tagging user. In order to evaluate the effectiveness of our model, two experiments are performed with datasets crawled from del.icio.us. Experimental results show that our model has a better performance than other approaches with respect to Normalized Discounted Cumulated Gain (NDCG). In addition, F-1 measure of tagged web page clustering performance is also increased when our model is applied to filter the noisy social tags with low tag confidence.

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References

  1. Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: The 16th International Conference on World Wide Web (WWW 2007), pp. 501–510. ACM Press, Banff (2007)

    Chapter  Google Scholar 

  2. Park, L., Ramamohanarao, K.: Mining Web Multi-resolution Community-based Popularity for Information Retrieval. In: The 16th International ACM Conference on Conference on Information and Knowledge Management (CIKM 2007), pp. 545–554. ACM Press, Lisboa (2007)

    Google Scholar 

  3. Wang, C., Zhang, L., Zhang, H.: Learning to Reduce the Semantic Gap in Web Image Retrieval and Annotation. In: The 31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 355–362. ACM Press, Singapore (2008)

    Google Scholar 

  4. Noll, M.G., Meinel, C.: Exploring social annotations for web document classification. In: The 2008 ACM Symposium on Applied Computing (SAC 2008), pp. 2315–2320. ACM Press, Brazil (2008)

    Chapter  Google Scholar 

  5. Ramage, D., Heymann, P.: Clustering the Tagged Web. In: The Second ACM International Conference on Web Search and Data Mining, pp. 54–63. ACM Press, Barcelona (2009)

    Chapter  Google Scholar 

  6. Xu, S., Bao, S., Cao, Y., Yu, Y.: Using social annotations to improve language model for information retrieval. In: The 16th International ACM Conference on Conference on Information and Knowledge Management (CIKM 2007), pp. 1003–1006. ACM Press, Lisboa (2007)

    Google Scholar 

  7. Mathes, A.: Folksonomies - cooperative classification and communication through shared metadata. Computer Mediated Communication, LIS590CMC (Doctoral Seminar), Graduate School of Library and Information Science, University of Illinois Urbana-Champaign (2004)

    Google Scholar 

  8. Lee, S., Min, H., Lee, Y.B., Ro, Y.M.: Measurement of Tag Confidence in User Generated Contents Retrieval. In: Proc. of SPIE, pp. 7257–7262 (2009)

    Google Scholar 

  9. Wu, L., Yang, L., Yu, N.: Learning to Tag. In: The 18th International Conference on World Wide Web (WWW 2009), pp. 361–370. ACM Press, Marid (2009)

    Chapter  Google Scholar 

  10. Zhou, D., Bian, J., Zheng, S., Zha, H., Giles, C.L.: Exploring social annotations for information retrieval. In: The 17th International Conference on World Wide Web (WWW 2008), pp. 715–724. ACM Press, Beijing (2008)

    Google Scholar 

  11. Schenkel, R., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Weikum, G.: Efficient top-k querying over social-tagging networks. In: The 31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 523–530. ACM Press, Singapore (2008)

    Google Scholar 

  12. Wu, X., Zhang, L., Yu, Y.: Exploring social annotations for the semantic web. In: The 15th International Conference on World Wide Web (WWW 2006), pp. 417–426. ACM Press, Edinburgh (2006)

    Chapter  Google Scholar 

  13. Brooks, C.H., Montanez, N.: Improved annotation of the blogosphere via autotagging and hierarchical clustering. In: The 15th International Conference on World Wide Web (WWW 2006), pp. 625–632. ACM Press, Edinburgh (2006)

    Chapter  Google Scholar 

  14. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: The 17th International Conference on World Wide Web (WWW 2008), pp. 675–684. ACM Press, Beijing (2008)

    Google Scholar 

  15. Krestel, R., Fankhauser, P., Nejdl, W.: Latent Dirichlet Allocation for Tag Recommendation. In: The 3rd ACM Conference on Recommender Systems, New York, USA (2009)

    Google Scholar 

  16. Körner, C., Benz, D., Hotho, A., Strohmaier, M., Stumme, B.: Stop Thinking, Start Tagging: Tag Semantics Emerge from Collaborative Verbosity. In: The International Conference on World Wide Web (WWW 2010), pp. 251–260. ACM Press, USA (2010)

    Google Scholar 

  17. Koutrika, G., Effendi, F.A., Gyöngyi, Z., Heymann, P., Molina, H.G.: Combating Spam in Tagging Systems. In: The Third International Workshop on Adversarial Information Retrieval on the Web (AIRWeb 2007), Alberta, Canada (2007)

    Google Scholar 

  18. Wu, L., Yang, L., Yu, N.: Learning to Tag. In: The 18th International Conference on World Wide Web (WWW 2009), pp. 361–370. ACM Press, Marid (2009)

    Chapter  Google Scholar 

  19. Liu, D., Hua, X., Yang, L.: Tag Ranking. In: The 18th International Conference on World Wide Web (WWW 2009), pp. 351–360. ACM Press, Marid (2009)

    Chapter  Google Scholar 

  20. Akamine, S., Kawahara, D., Kato, Y.: WISDOM: A Web Information Credibility Analysis System. In: Proceedings of the ACL-IJCNLP, Suntec, Singapore, pp. 1–4 (2009)

    Google Scholar 

  21. Zhu, J., Wang, C., He, X., Bu, J., Chen, C., Qu, M., Lu, G.: Tag-Oriented Document Summarization. In: The 18th International Conference on World Wide Web (WWW 2009), pp. 1195–1196. ACM Press, Marid (2009)

    Chapter  Google Scholar 

  22. Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the Semantic Web: Collaborative Tag Suggestions. In: The 15th International Conference on World Wide Web (WWW 2006). ACM Press, Edinburgh (2006)

    Google Scholar 

  23. Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating similarity measures for emergent semantics of social tagging. In: The 18th International Conference on World Wide Web (WWW 2009), pp. 641–650. ACM Press, Marid (2009)

    Chapter  Google Scholar 

  24. Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–466 (2002)

    Article  Google Scholar 

  25. Manning, C., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent and Distributed Computing Lab, College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China

    Xiwu Gu, Xianbing Wang, Ruixuan Li, Kunmei Wen & Yufei Yang

  2. Department of Electrical and Computer Engineering, University of Minnesota, twin cities 200 Union Street SE, Minneapolis, MN, 55455, US

    Weijun Xiao

Authors
  1. Xiwu Gu
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  2. Xianbing Wang
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  3. Ruixuan Li
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  4. Kunmei Wen
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  5. Yufei Yang
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  6. Weijun Xiao
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Editor information

Editors and Affiliations

  1. Microsoft Research Asia, 5 Danling Rd., Haidian District, 100190, Beijing, China

    Haixun Wang

  2. Computer School, Wuhan University, 16 Luojiashan Road, 430072, Hubei, China

    Shijun Li

  3. Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, 060-0814, Hokkaido, Sapporo, Japan

    Satoshi Oyama

  4. College of Information Science and Technology, Drexel University, 19104, Philadelphia, PA, USA

    Xiaohua Hu

  5. State Key Laboratory of Software Engineering, Wuhan University, 16 Luojiashan Road, 430072, Wuhan, Hubei, China

    Tieyun Qian

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© 2011 Springer-Verlag Berlin Heidelberg

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Gu, X., Wang, X., Li, R., Wen, K., Yang, Y., Xiao, W. (2011). Measuring Social Tag Confidence: Is It a Good or Bad Tag?. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-23535-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23534-4

  • Online ISBN: 978-3-642-23535-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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