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Event Detection and Aspects in Twitter: A BoW Approach

  • Abhaya Kumar Pradhan
  • Hrushikesha Mohanty
  • Rajendra Prasad Lal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)

Abstract

Tweets carry much information on the context people are tweeting. Finding the context of tweets or finding the event the tweets talk of is a hot research problem. Among several techniques like statistical, graph-based, machine learning, NLP based and many such techniques, bag-of-words technique is simple and elegant. This paper reports an event detection technique using clustering of bag-of-words of a given set of tweets. The method proposed follows three phase incremental clustering applying Jaccard similarity and Simpson similarity coefficients at different phases. Further, our method is capable of detecting different aspects of an event using a heuristic called EAAS (Event And Aspects Selection) based on tweeter participation, cluster quality and word commonality with the detected event. As a case study, we have used publicly available tweets, collected from Twitter streaming API with a keyword-based strategy. The obtained event detection result is presented and aspects of an event are evaluated in terms of precision and recall against human annotators. With concluding remark the paper presents its findings the possibility of enhancement in event detection as well as aspect finding capability.

Keywords

Event detection Aspect detection Social network mining Microblog mining Tweet processing 

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Aiello, L.M., et al.: Sensing trending topics in Twitter. IEEE Trans. Multimedia 15(6), 1268–1282 (2013).  https://doi.org/10.1109/TMM.2013.2265080CrossRefGoogle Scholar
  3. 3.
    Aldhaheri, A., Lee, J.: Event detection on large social media using temporal analysis. In: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–6, January 2017.  https://doi.org/10.1109/CCWC.2017.7868467
  4. 4.
    Allan, J., Lavrenko, V., Jin, H.: First story detection in TDT is hard. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, CIKM 2000, pp. 374–381. ACM, New York (2000).  https://doi.org/10.1145/354756.354843
  5. 5.
    Alsaedi, N., Burnap, P., Rana, O.: Can we predict a riot? Disruptive event detection using Twitter. ACM Trans. Internet Technol. 17(2), 18:1–18:26 (2017).  https://doi.org/10.1145/2996183CrossRefGoogle Scholar
  6. 6.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015).  https://doi.org/10.1111/coin.12017MathSciNetCrossRefGoogle Scholar
  7. 7.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: Real-world event identification on Twitter (2011). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2745
  8. 8.
    Brants, T., Chen, F., Farahat, A.: A system for new event detection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003, pp. 330–337. ACM, New York (2003).  https://doi.org/10.1145/860435.860495
  9. 9.
    Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on Twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, MDMKDD 2010, pp. 4:1–4:10. ACM, New York (2010).  https://doi.org/10.1145/1814245.1814249
  10. 10.
    Fung, G.P.C., Yu, J.X., Yu, P.S., Lu, H.: Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB 2005, pp. 181–192. VLDB Endowment (2005). http://dl.acm.org/citation.cfm?id=1083592.1083616
  11. 11.
    Guille, A., Favre, C.: Event detection, tracking, and visualization in twitter: a mention-anomaly-based approach. Soc. Netw. Anal. Mining 5(1), 18 (2015).  https://doi.org/10.1007/s13278-015-0258-0CrossRefGoogle Scholar
  12. 12.
    Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 91–101. ACM, New York (2002).  https://doi.org/10.1145/775047.775061
  13. 13.
    Kumar, S., Liu, H., Mehta, S., Subramaniam, L.V.: Exploring a scalable solution to identifying events in noisy twitter streams. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM 2015, pp. 496–499. ACM, New York (2015).  https://doi.org/10.1145/2808797.2809389
  14. 14.
    Landis, J., Koch, G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977).  https://doi.org/10.2307/2529310CrossRefzbMATHGoogle Scholar
  15. 15.
    Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995).  https://doi.org/10.1145/219717.219748CrossRefGoogle Scholar
  16. 16.
    O’Connor, B.T., Krieger, M., Ahn, D.: TweetMotif: exploratory search and topic summarization for Twitter. In: ICWSM (2010)Google Scholar
  17. 17.
    Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to Twitter. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 181–189. Association for Computational Linguistics, Stroudsburg (2010). http://dl.acm.org/citation.cfm?id=1857999.1858020
  18. 18.
    Phuvipadawat, S., Murata, T.: Breaking news detection and tracking in Twitter. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 120–123, August 2010.  https://doi.org/10.1109/WI-IAT.2010.205
  19. 19.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).  https://doi.org/10.1016/0377-0427(87)90125-7. http://www.sciencedirect.com/science/article/pii/0377042787901257CrossRefzbMATHGoogle Scholar
  20. 20.
    Rudra, K., Goyal, P., Ganguly, N., Mitra, P., Imran, M.: Identifying sub-events and summarizing disaster-related information from microblogs. In: The 41st International ACM SIGIR Conference on Research & #38; Development in Information Retrieval, SIGIR 2018, pp. 265–274. ACM, New York (2018).  https://doi.org/10.1145/3209978.3210030
  21. 21.
    Salas, A., Georgakis, P., Nwagboso, C., Ammari, A., Petalas, I.: Traffic event detection framework using social media. In: 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC), pp. 303–307, July 2017.  https://doi.org/10.1109/ICSGSC.2017.8038595
  22. 22.
    Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams (2009). https://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/170
  23. 23.
    Srijith, P., Hepple, M., Bontcheva, K., Preotiuc-Pietro, D.: Sub-story detection in Twitter with hierarchical dirichlet processes. Inf. Process. Manag. 53(4), 989–1003 (2017).  https://doi.org/10.1016/j.ipm.2016.10.004CrossRefGoogle Scholar
  24. 24.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co. Inc., Boston (2005)Google Scholar
  25. 25.
    Unankard, S., Li, X., Sharaf, M.A.: Emerging event detection in social networks with location sensitivity. World Wide Web 18(5), 1393–1417 (2015).  https://doi.org/10.1007/s11280-014-0291-3CrossRefGoogle Scholar
  26. 26.
    Xie, W., Zhu, F., Jiang, J., Lim, E.P., Wang, K.: TopicSketch: real-time bursty topic detection from Twitter. IEEE Trans. Knowl. Data Eng. 28(8), 2216–2229 (2016).  https://doi.org/10.1109/TKDE.2016.2556661CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer and Information SciencesUniversity of HyderabadHyderabadIndia

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