User Interests Identification on Twitter Using a Hierarchical Knowledge Base

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)


Twitter, due to its massive growth as a social networking platform, has been in focus for the analysis of its user generated content for personalization and recommendation tasks. A common challenge across these tasks is identifying user interests from tweets. Semantic enrichment of Twitter posts, to determine user interests, has been an active area of research in the recent past. These approaches typically use available public knowledge-bases (such as Wikipedia) to spot entities and create entity-based user profiles. However, exploitation of such knowledge-bases to create richer user profiles is yet to be explored. In this work, we leverage hierarchical relationships present in knowledge-bases to infer user interests expressed as a Hierarchical Interest Graph. We argue that the hierarchical semantics of concepts can enhance existing systems to personalize or recommend items based on a varied level of conceptual abstractness. We demonstrate the effectiveness of our approach through a user study which shows an average of approximately eight of the top ten weighted hierarchical interests in the graph being relevant to a user’s interests.


#eswc2014Kapanipathi User Profiles Personalization Social Web Semantics Twitter Wikipedia Hierarchical Interest Graph 


  1. 1.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing User Modeling on Twitter for Personalized News Recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. 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 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)Google Scholar
  3. 3.
    Albakour, M.-D., Macdonald, C., Ounis, I.: On Sparsity and Drift for Effective Real-time Filtering in Microblogs. In: CIKM 2013 (2013)Google Scholar
  4. 4.
    Collins, A.M., Loftus, E.F.: A spreading-activation theory of semantic processing. Psychological Review 82(6), 407–428 (1975)CrossRefGoogle Scholar
  5. 5.
    Crestani, F.: Application of Spreading Activation Techniques in InformationRetrieval. Artificial Intellence ReviewGoogle Scholar
  6. 6.
    Derczynski, L., Maynard, D., Aswani, N., Bontcheva, K.: Microblog-genre Noise and Impact on Semantic Annotation Accuracy. In: HT 2013 (2013)Google Scholar
  7. 7.
    Genc, Y., Sakamoto, Y., Nickerson, J.V.: Discovering Context: Classifying Tweets Through a Semantic Transform Based on Wikipedia. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) FAC 2011. LNCS, vol. 6780, pp. 484–492. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Godoy, D., Amandi, A.: Modeling User Interests by Conceptual Clustering. Inf. Syst. (2006)Google Scholar
  9. 9.
    Harvey, M., Crestani, F., Carman, M.J.: Building User Profiles from Topic Models for Personalised Search. In: CIKM 2013 (2013)Google Scholar
  10. 10.
    Hinton, G.E.: Parallel Models of Associative Memory (1989)Google Scholar
  11. 11.
    Jain, P., Hitzler, P., Sheth, A.P., Verma, K., Yeh, P.Z.: Ontology Alignment for Linked Open Data. 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. 402–417. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kapanipathi, P., Orlandi, F., Sheth, A.P., Passant, A.: Personalized Filtering of the Twitter Stream. In: SPIM Workshop at ISWC 2011 (2011)Google Scholar
  13. 13.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval (2008)Google Scholar
  14. 14.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia Spotlight: Shedding Light on the Web of Documents. In: I-Semantics 2011 (2011)Google Scholar
  15. 15.
    Michelson, M., Macskassy, S.A.: Discovering Users’ Topics of Interest on Twitter: A First Look. In: AND 2010 (2010)Google Scholar
  16. 16.
    Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You Are Who You Know: Inferring User Profiles in Online Social Networks. In: WSDM 2010 (2010)Google Scholar
  17. 17.
    Orlandi, F., Breslin, J., Passant, A.: Aggregated, Interoperable and Multi-domain User Profiles for the Social Web. In: I-SEMANTICS 2012 (2012)Google Scholar
  18. 18.
    Ponzetto, S.P., Strube, M.: Deriving a Large Scale Taxonomy from Wikipedia. In: AAAI 2007 (2007)Google Scholar
  19. 19.
    Qiu, F., Cho, J.: Automatic Identification of User Interest for Personalized Search. In: WWW 2006 (2006)Google Scholar
  20. 20.
    Quilian, M.R.: Semantic Memory. In: M. Minski (ed.). Semantic Information Processing. MIT Press, Cambridge (1968)Google Scholar
  21. 21.
    Ramage, D., Dumais, S.T., Liebling, D.J.: Characterizing Microblogs with Topic Models. In: ICWSM 2010 (2010)Google Scholar
  22. 22.
    Ramanathan, K., Kapoor, K.: Creating User Profiles Using Wikipedia. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds.) ER 2009. LNCS, vol. 5829, pp. 415–427. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    Ritter, A., Clark, S., Mausam, Etzioni, O.: Named entity recognition in tweets: An experimental study. In: EMNLP 2011 (2011)Google Scholar
  24. 24.
    Schonhofen, P.: Identifying Document Topics Using the Wikipedia Category Network. In: WI 2006 (2006)Google Scholar
  25. 25.
    Sieg, A., Mobasher, B., Burke, R.: Web Search Personalization with Ontological User Profiles. In: CIKM 2007 (2007)Google Scholar
  26. 26.
    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short Text Classification in Twitter to Improve Information Filtering. In: SIGIR 2010 (2010)Google Scholar
  27. 27.
    Tao, K., Abel, F., Gao, Q., Houben, G.-J.: TUMS: Twitter-Based User Modeling Service. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 269–283. Springer, Heidelberg (2012)Google Scholar
  28. 28.
    Xu, T., Oard, D.W.: Wikipedia-based Topic Clustering for Microblogs. In: Proceedings of the American Society for Information Science and Technology (2011)Google Scholar
  29. 29.
    Xu, Y., Wang, K., Zhang, B., Chen, Z.: Privacy-enhancing Personalized Web Search. In: WWW 2007 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Kno.e.sis CenterWright State UniversityUSA
  2. 2.IBM TJ Watson Research CenterUSA

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