Learning Semantic Relationships between Entities in Twitter

  • Ilknur Celik
  • Fabian Abel
  • Geert-Jan Houben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6757)

Abstract

In this paper, we investigate whether semantic relationships between entities can be learnt from analyzing microblog posts published on Twitter. We identify semantic links between persons, products, events and other entities. We develop a relation discovery framework that allows for the detection of typed relations that moreover may have temporal dynamics. Based on a large Twitter dataset, we evaluate different strategies and show that co-occurrence based strategies allow for high precision and perform particularly well for relations between persons and events achieving precisions of more than 80%. We further analyze the performance in learning relationships that are valid only for a certain time period and reveal that for those types of relationships Twitter is a suitable source as it allows for discovering trending topics with higher accuracy and with lower delay in time than traditional news media.

Keywords

semantic enrichment relation learning twitter social web 

References

  1. 1.
    Abel, F., Celik, I.: Supporting website: datasets, further details and additional findings (2011), http://wis.ewi.tudelft.nl/icwe2011/relation-learning/
  2. 2.
    Abel, F., Gao, Q., Houben, G.J., Tao, K.: Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web. In: Antoniou, et al. (eds.) Extended Semantic Web Conference (ESWC), Heraklion, Greece, Springer, Heidelberg (2011)Google Scholar
  3. 3.
    Akcora, C.G., Bayir, M.A., Demirbas, M., Ferhatosmanoglu, H.: Identifying Breakpoints in Public Opinion. In: Melvile, P., Leskovec, J., Provost, F. (eds.) Proceedings of Workshop on Social Media Analytics (SOMA) at KDD 2010, Washington, DC, USA (2010)Google Scholar
  4. 4.
    Bernstein, M., Kairam, S., Suh, B., Hong, L., Chi, E.H.: A torrent of tweets: managing information overload in online social streams. In: Proceedings of the CHI Workshop on Microblogging: What and How Can We Learn From It? (2010)Google Scholar
  5. 5.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - A crystallization point for the Web of Data. In: Web Semantics: Science, Services and Agents on the World Wide Web (2009)Google Scholar
  6. 6.
    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.) Proceedings of the Fourth International Conference on Weblogs and Social Media (ICWSM). The AAAI Press, Washington, DC, USA (2010)Google Scholar
  7. 7.
    Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems (CHI), pp. 1185–1194. ACM, New York (2010)Google Scholar
  8. 8.
    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: Proceedings of the 19th International Conference on World Wide Web (WWW), pp. 331–340. ACM, New York (2010)Google Scholar
  9. 9.
    Honeycutt, C., Herring, S.C.: Beyond microblogging: Conversation and collaboration via twitter. In: Proceedings of the 42nd Hawaii International Conference on Systems Science (HICSS), pp. 1–10. IEEE, Big Island (2009)Google Scholar
  10. 10.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Emergent Semantics in BibSonomy. In: Hochberger, C., Liskowsky, R. (eds.) Informatik 2006: Informatik für Menschen. LNI, vol. 94(2). GI, Bonn (2006)Google Scholar
  11. 11.
    Huang, J., Thornton, K.M., Efthimiadis, E.N.: Conversational Tagging in Twitter. In: Chignell, M.H., Toms, E. (eds.) Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT), pp. 173–178. ACM, New York (2010)CrossRefGoogle Scholar
  12. 12.
    Hughes, A.L., Palen, L.: Twitter Adoption and Use in Mass Convergence and Emergency Events. In: Landgren, J., Jul, S. (eds.) Proceedings of the International Conference on Information Systems for Crisis Response and Management ISCRAM 2009 (May 2009)Google Scholar
  13. 13.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. WebKDD/SNA-KDD 2007, pp. 56–65. ACM, New York (2007)Google Scholar
  14. 14.
    Kaufman, S.J., Chen, J.: Where we Twitter. In: Proceedings of the CHI Workshop on Microblogging: What and How Can We Learn From It? (2010)Google Scholar
  15. 15.
    Kohlschütter, C., Fankhauser, P., Nejdl, W.: Boilerplate detection using shallow text features. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM), pp. 441–450. ACM, New York (2010)CrossRefGoogle Scholar
  16. 16.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web (WWW 2010), pp. 591–600. ACM, New York (2010)Google Scholar
  17. 17.
    Lerman, K., Ghosh, R.: Information contagion: an empirical study of spread of news on digg and twitter social networks. In: Proceedings of 4th International Conference on Weblogs and Social Media, ICWSM (2010)Google Scholar
  18. 18.
    Marinho, L.B., Buza, K., Schmidt-Thieme, L.: Folksonomy-based collabulary learning. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T.W, Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 261–276. Springer, Heidelberg (2008)Google Scholar
  19. 19.
    Owens, J.W., Lenz, K., Speagle, S.: Trick or Tweet: How Usable is Twitter for First-Time Users? Usability News 11 (2009)Google Scholar
  20. 20.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web (WWW). ACM, New York (2011)Google Scholar
  21. 21.
    Rowe, M., Stankovic, M., Laublet, P.: Mapping Tweets to Conference Talks: A Goldmine for Semantics. In: Passant, A., Breslin, J., Fernandez, S., Bojars, U. (eds.) Workshop on Social Data on the Web (SDoW), Colocated with ISWC 2010, CEUR-WS.org, Shanghai, China, vol. 664 (2010)Google Scholar
  22. 22.
    Yardi, S., boyd, d.: Dynamic Debates: An Analysis of Group Polarization over Time on Twitter. Bulletin of Science, Technology and Society 30 (2010)Google Scholar
  23. 23.
    Zhao, D., Rosson, M.B.: How and why people Twitter: the role that micro-blogging plays in informal communication at work. In: Proceedings of the ACM International Conference on Supporting Group Work (GROUP), pp. 243–252. ACM, New York (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ilknur Celik
    • 1
  • Fabian Abel
    • 1
  • Geert-Jan Houben
    • 1
  1. 1.Web Information SystemsDelft University of TechnologyDelftThe Netherlands

Personalised recommendations