Advertisement

Characterising Emergent Semantics in Twitter Lists

  • Andrés García-Silva
  • Jeon-Hyung Kang
  • Kristina Lerman
  • Oscar Corcho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)

Abstract

Twitter lists organise Twitter users into multiple, often overlapping, sets. We believe that these lists capture some form of emergent semantics, which may be useful to characterise. In this paper we describe an approach for such characterisation, which consists of deriving semantic relations between lists and users by analyzing the co-occurrence of keywords in list names. We use the vector space model and Latent Dirichlet Allocation to obtain similar keywords according to co-occurrence patterns. These results are then compared to similarity measures relying on WordNet and to existing Linked Data sets. Results show that co-occurrence of keywords based on members of the lists produce more synonyms and more correlated results to that of WordNet similarity measures.

Keywords

Path Length Semantic Relation Latent Dirichlet Allocation Vector Space Model SPARQL Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abel, F., Celik, I., Houben, G.-J., Siehndel, P.: Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 1–17. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  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, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 201. LNCS, vol. 6644, Part II, pp. 375–389. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The Story So Far. International Journal on Semantic Web and Information Systems, IJSWIS (2009)Google Scholar
  4. 4.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - A crystallization point for the Web of Data. Journal of Web Semantic 7(3), 154–165 (2009)CrossRefGoogle Scholar
  5. 5.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  6. 6.
    Cano, A.E., Tucker, S., Ciravegna, F.: Follow me: Capturing entity-based semantics emerging from personal awareness streams. In: Making Sense of Microposts (#MSM 2011), pp. 33–44 (2011)Google Scholar
  7. 7.
    Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 615–631. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Choudhury, S., Breslin, J.: Extracting semantic entities and events from sports tweets. In: Proceedings of the ESWC 2011 Workshop on ’Making Sense of Microposts’. CEUR Workshop Proceedings, vol. 718 (May 2011)Google Scholar
  9. 9.
    Dongwoo Kim, Y.J.: Analysis of Twitter lists as a potential source for discovering latent characteristics of users. In: Workshop on Microblogging at the ACM Conference on Human Factors in Computer Systems (CHI 2010), Atlanta, CA, USA (2010)Google Scholar
  10. 10.
    Fellbaum, C.: WordNet and wordnets, 2nd edn., pp. 665–670. Elsevier, Oxford (2005)Google Scholar
  11. 11.
    García-Silva, A., Corcho, O., Alani, H., Gómez-Pérez, A.: Review of the state of the art: discovering and associating semantics to tags in folksonomies. The Knowledge Engineering Review 27(01), 57–85 (2012)CrossRefGoogle Scholar
  12. 12.
    García-Silva, A., Szomszor, M., Alani, H., Corcho, O.: Preliminary results in tag disambiguation using dbpedia. In: Knowledge Capture (K-Cap 2009)-Workshop on Collective Knowledge Capturing and Representation-CKCaR (2009)Google Scholar
  13. 13.
    Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006)CrossRefGoogle Scholar
  14. 14.
    Heim, P., Lohmann, S., Stegemann, T.: Interactive Relationship Discovery via the Semantic Web. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 303–317. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. CoRR, cmp-lg/9709008 (1997)Google Scholar
  17. 17.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the First Workshop on Online Social Networks, WOSN 2008, pp. 19–24. ACM, New York (2008)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Gerd, S.: Evaluating similarity measures for emergent semantics of social tagging. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 641–650. ACM, New York (2009)CrossRefGoogle Scholar
  20. 20.
    Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, pp. 31–40. ACM Press (2006)Google Scholar
  21. 21.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: Dbpedia spotlight: Shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, I-Semantics (2011)Google Scholar
  22. 22.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: Similarity - measuring the relatedness of concepts. In: AAAI, pp. 1024–1025. AAAI Press / The MIT Press (2004)Google Scholar
  23. 23.
    Rowe, M., Stankovic, M.: Mapping tweets to conference talks: A goldmine for semantics. In: Social Data on the Web Workshop, International Semantic Web Conference (2010)Google Scholar
  24. 24.
    Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)Google Scholar
  25. 25.
    Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on twitter. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 705–714. ACM, New York (2011)CrossRefGoogle Scholar
  26. 26.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proc. of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrés García-Silva
    • 1
  • Jeon-Hyung Kang
    • 2
  • Kristina Lerman
    • 2
  • Oscar Corcho
    • 1
  1. 1.Ontology Engineering Group, Facultad de InformáticaUniversidad Politécnica de MadridSpain
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaUSA

Personalised recommendations