Identification and Ranking of Event-Specific Entity-Centric Informative Content from Twitter

  • Debanjan Mahata
  • John R. Talburt
  • Vivek Kumar Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9103)

Abstract

Twitter has become the leading platform for mining information related to real-life events. A large amount of the shared content in Twitter are non-informative spams and informal personal updates. Thus, it is necessary to identify and rank informative event-specific content from Twitter. Moreover, tweets containing information about named entities (like person, place, organization, etc.) occurring in the context of an event, generates interest and aids in gaining useful insights. In this paper, we develop a novel generic model based on the principle of mutual reinforcement, for representing and identifying event-specific, as well as entity-centric informative content from Twitter. An algorithm is proposed that ranks tweets in terms of event-specific, entity-centric information content by leveraging the semantics of relationships between different units of the model.

Keywords

Informative Content Mutual Reinforcement Affinity Matrix Initial Score Australian Prime Minister 
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.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: ICWSM, vol. 11, pp. 438–441 (2011)Google Scholar
  2. 2.
    Olteanu, A., Castillo, C., Diaz, F., Vieweg, S.: Crisislex: a lexicon for collecting and filtering microblogged communications in crises. In: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM 2014), number EPFL-CONF-203561 (2014)Google Scholar
  3. 3.
    Popescu, A.-M., Pennacchiotti, M., Paranjpe, D.: Extracting events and event descriptions from twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 105–106. ACM (2011)Google Scholar
  4. 4.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans. Knowl. Data Eng. 25(4), 919–931 (2013)CrossRefGoogle Scholar
  5. 5.
    Shaw, R., Troncy, R., Hardman, L.: LODE: linking open descriptions of events. In: Gómez-Pérez, A., Yu, Y., Ding, Y. (eds.) ASWC 2009. LNCS, vol. 5926, pp. 153–167. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  6. 6.
    Wei, F., Li, W., Lu, Q., He, Y.: Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 283–290. ACM (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Debanjan Mahata
    • 1
  • John R. Talburt
    • 1
  • Vivek Kumar Singh
    • 2
  1. 1.Department of Information ScienceUniversity of Arkansas at Little Rock Little RockUSA
  2. 2.Department of Computer ScienceSouth Asian UniversityNew DelhiIndia

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