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Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web

  • Fabian Abel
  • Qi Gao
  • Geert-Jan Houben
  • Ke Tao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6644)

Abstract

As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem.

In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web.

Keywords

semantic enrichment twitter user profile construction news linkage 

References

  1. 1.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proc. of 19th Int. Conf. on World Wide Web, pp. 851–860. ACM, New York (2010)CrossRefGoogle Scholar
  2. 2.
    Gaffney, D.: #iranElection: quantifying online activism. In: Proc. of the WebSci10: Extending the Frontiers of Society On-Line (2010)Google Scholar
  3. 3.
    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: Proc. of 19th Int. Conf. on World Wide Web, pp. 331–340. ACM, New York (2010)CrossRefGoogle Scholar
  4. 4.
    Lerman, K., Ghosh, R.: Information contagion: an empirical study of spread of news on Digg and Twitter social networks. In: Cohen, W.W., Gosling, S. (eds.) Proc. of 4th Int. Conf. on Weblogs and Social Media. AAAI Press, Menlo Park (2010)Google Scholar
  5. 5.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proc. of the 19th Int. Conf. on World Wide Web, pp. 591–600. ACM, New York (2010)CrossRefGoogle Scholar
  6. 6.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential Twitterers. In: Davison, B.D., Suel, T., Craswell, N., Liu, B. (eds.) Proc. of 3rd ACM Int. Conf. on Web Search and Data Mining, pp. 261–270. ACM, New York (2010)Google Scholar
  7. 7.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. In: Cohen, W.W., Gosling, S. (eds.) Proc. of 4th Int. Conf. on Weblogs and Social Media. AAAI Press, Menlo Park (2010)Google Scholar
  8. 8.
    Lee, K., Caverlee, J., Webb, S.: The social honeypot project: protecting online communities from spammers. In: Proc. of 19th Int. Conf. on World Wide Web, pp. 1139–1140. ACM, New York (2010)CrossRefGoogle Scholar
  9. 9.
    Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: Proc. of 33rd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 435–442. ACM, New York (2010)Google Scholar
  10. 10.
    Huang, J., Thornton, K.M., Efthimiadis, E.N.: Conversational tagging in twitter. In: Proc. of 21st Conf. on Hypertext and Hypermedia, pp. 173–178. ACM, New York (2010)CrossRefGoogle Scholar
  11. 11.
    Laniado, D., Mika, P.: Making sense of twitter. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 470–485. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Efron, M.: Hashtag retrieval in a microblogging environment. In: Proc. of 33rd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 787–788. ACM, New York (2010)Google Scholar
  13. 13.
    Passant, A., Hastrup, T., Bojars, U., Breslin, J.: Microblogging: A Semantic Web and Distributed Approach. In: Bizer, C., Auer, S., Grimnes, G.A., Heath, T. (eds.) Proc. of 4th Workshop Scripting For the Semantic Web (SFSW 2008) co-located with ESWC 2008, vol. 368 (2008), CEUR-WS.org
  14. 14.
    Passant, A., Laublet, P.: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data. In: Proceedings of the WWW 2008 Workshop Linked Data on the Web (LDOW 2008), Beijing, China (2008)Google Scholar
  15. 15.
    Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proc. of 28th Int. Conf. on Human Factors in Computing Systems, pp. 1185–1194. ACM, New York (2010)Google Scholar
  16. 16.
    Jadhav, A., Purohit, H., Kapanipathi, P., Ananthram, P., Ranabahu, A., Nguyen, V., Mendes, P.N., Smith, A.G., Cooney, M., Sheth, A.: Twitris 2.0: Semantically empowered system for understanding perceptions from social data. In: Proc. of the Int. Semantic Web Challenge (2010)Google Scholar
  17. 17.
    Mendes, P.N., Passant, A., Kapanipathi, P.: Twarql: tapping into the wisdom of the crowd. In: Proc. of the 6th International Conference on Semantic Systems, pp. 45:1–45:3. ACM, New York (2010)Google Scholar
  18. 18.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: Proc. of 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51. ACM, New York (2009)Google Scholar
  19. 19.
    Mendoza, M., Poblete, B., Castillo, C.: Twitter Under Crisis: Can we trust what we RT? In: Proc. of 1st Workshop on Social Media Analytics (SOMA 2010). ACM Press, New York (2010)Google Scholar
  20. 20.
    Kohlschütter, C., Fankhauser, P., Nejdl, W.: Boilerplate detection using shallow text features. In: Proc. of 3rd ACM Int. Conf. on Web Search and Data Mining, pp. 441–450. ACM, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabian Abel
    • 1
  • Qi Gao
    • 1
  • Geert-Jan Houben
    • 1
  • Ke Tao
    • 1
  1. 1.Web Information SystemsDelft University of TechnologyThe Netherlands

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