TUMS: Twitter-Based User Modeling Service

  • Ke Tao
  • Fabian Abel
  • Qi Gao
  • Geert-Jan Houben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7117)


Twitter is today’s most popular micro-blogging service on the Social Web. As people discuss various fresh topics, Twitter messages (tweets) can tell much about the current interests and concerns of a user. In this paper, we introduce TUMS, a Twitter-based User Modeling Service, that infers semantic user profiles from the messages people post on Twitter. It features topic detection and entity extraction for tweets and allows for further enrichment by linking tweets to news articles that describe the context of the tweets. TUMS is made publicly available as a Web application. It allows end-users to overview Twitter-based profiles in a structured way and allows them to see in which topics or entities a user was interested at a specific point in time. Furthermore, it provides Twitter-based user profiles in RDF format and allows applications to incorporate these profiles in order to adapt their functionality to the current interests of a user. TUMS is available via:


user modeling twitter semantic enrichment service 


  1. 1.
    Jameson, A.: Adaptive interfaces and agents. The HCI handbook: fundamentals, evolving technologies and emerging applications, pp. 305–330 (2003)Google Scholar
  2. 2.
    Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): Adaptive Web 2007. LNCS, vol. 4321. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  3. 3.
    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
  4. 4.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the first workshop on Online social networks. In: WOSP 2008, pp. 19–24. ACM, New York (2008)Google Scholar
  5. 5.
    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
  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.) Proceedings of the Third International Conference on Web Search and Web Data Mining (WSDM 2010), pp. 261–270. ACM, New York (2010)CrossRefGoogle 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.) Proceedings of the Fourth International Conference on Weblogs and Social Media (ICWSM 2010). The AAAI Press, Washington, DC, USA (2010)Google Scholar
  8. 8.
    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 2010), pp. 1185–1194. ACM, New York (2010)Google Scholar
  9. 9.
    Gaffney, D.: #iranElection: quantifying online activism. In: Proceedings of the WebSci10: Extending the Frontiers of Society On-Line (2010)Google Scholar
  10. 10.
    Diakopoulos, N.A., Shamma, D.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, pp. 1195–1198. ACM, New York (2010)Google Scholar
  11. 11.
    Stankovic, M., Rowe, 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 2010), co-located with ISWC 2010, Shanghai, China, vol. 664. (2010)Google Scholar
  12. 12.
    Abel, F., Gao, Q., Houben, G.J., Tao, K.: Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web. In: García-Castro, R., et al. (eds.) ESWC 2011 Workshops. LNCS, vol. 7117, pp. 269–283. Springer, Heidelberg (2011)Google Scholar
  13. 13.
    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
  14. 14.
    Brickley, D., Miller, L.: FOAF Vocabulary Specification 0.91. Namespace document, FOAF Project (November 2007),
  15. 15.
    Bojars, U., Breslin, J.G.: SIOC Core Ontology Specification. Namespace document, DERI, NUI Galway (January 2009),
  16. 16.
    Brickley, D., Miller, L., Inkster, T., Zeng, Y., Wang, Y., Damljanovic, D., Huang, Z., Kinsella, S., Breslin, J., Ferris, B.: The Weighted Interests Vocabulary 0.5. Namespace document, Sourceforge (September 2010),
  17. 17.
    Kobsa, A.: Generic user modeling systems. User Modeling and User-Adapted Interaction 11(1-2), 49–63 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-Adapted Interaction (UMUAI) 18(3), 245–286 (2008)CrossRefGoogle Scholar
  19. 19.
    Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Information Sciences: an International Journal 179(1-2), 16–32 (2009)CrossRefGoogle Scholar
  20. 20.
    Abel, F., Henze, N., Herder, E., Krause, D.: Interweaving Public User Profiles on the Web. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 16–27. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Mehta, B.: Learning from What Others Know: Privacy Preserving Cross System Personalization. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 57–66. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010)Google Scholar
  23. 23.
    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 (May 2010)Google Scholar
  24. 24.
    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: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 331–340. ACM, New York (2010)Google Scholar
  25. 25.
    Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Rich, C., Yang, Q., Cavazza, M., Zhou, M.X. (eds.) Proceeding of the 14th International Conference on Intelligent User Interfaces (IUI 2010), pp. 31–40. ACM, New York (2010)CrossRefGoogle Scholar
  26. 26.
    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.) Proceedings of the the 4th Workshop Scripting For the Semantic Web (SFSW 2008) co-located with ESWC 2008, Tenerife, Spain, vol. 368. (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

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

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