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Innovations in News Media: Crisis Classification System

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9728))

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Abstract

Research in crisis management is a relatively new area of study, originating in the 1980s. Researchers have created several different models that separate organizational crises into discrete stages, such as pre-crisis, crisis and post-crisis. In this article we discuss a natural language based crisis detection system which classifies news articles relating to crises into the appropriate crisis stage. We use news articles from the New York Times as a source of training data, and use this data along with state of the art data mining and machine learning algorithms as the core of the system. In the future, our system may be expanded to identify and evaluate crisis management strategies, suggest crisis management strategies for the current state of a crisis, or provide stakeholders with summaries of crises in news media.

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References

  1. Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D.: An evaluation of Naive Bayesian anti-spam filtering. arXiv preprint cs/0006013 (2000)

    Google Scholar 

  2. Apache: Apache Maven Project. http://maven.apache.org/index.html

  3. Barboza, D.: Explosion at Apple Supplier Caused by Dust, China Says (2011). http://www.nytimes.com/2011/05/25/technology/25foxconn.html

  4. Berndt, D.J., Fisher, J.W., Craighead, J.G., Hevner, A.R., Luther, S., Studnicki, J.: The role of data warehousing in bioterrorism surveillance. Decis. Support Syst. 43(4), 1383–1403 (2007)

    Article  Google Scholar 

  5. Berndt, D.J., Fisher, J.W., Hevner, A.R., Studnicki, J.: Healthcare data warehousing and quality assurance. Computer 34(12), 56–65 (2001)

    Article  Google Scholar 

  6. Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: Crime data mining: a general framework and some examples. Computer 37(4), 50–56 (2004)

    Article  Google Scholar 

  7. Coombs, W.T.: Ongoing Crisis Communication: Planning, Managing, and Responding. Sage Publications, Thousand Oaks (1999)

    Google Scholar 

  8. Coombs, W.T.: Protecting organization reputations during a crisis: the development and application of situational crisis communication theory. Corp. Reput. Rev. 10(3), 163–176 (2007)

    Article  Google Scholar 

  9. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)

    Google Scholar 

  10. Etzioni, O., Cafarella, M., Downey, D., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165(1), 91–134 (2005)

    Article  Google Scholar 

  11. French, S., Niculae, C.: Believe in the model: mishandle the emergency. J. Homeland Secur. Emerg. Manag. 2(1), 1–16 (2005)

    Google Scholar 

  12. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, pp. 1–12, Stanford (2009)

    Google Scholar 

  13. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

    Article  Google Scholar 

  14. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, Waltham (2012)

    MATH  Google Scholar 

  15. Harms, S.K., Deogun, J., Saquer, J., Tadesse, T.: Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. In: Proceedings IEEE International Conference on Data Mining (ICDM 2001), pp. 603–606. IEEE (2001)

    Google Scholar 

  16. Jain, G., Ginwala, A., Aslandogan, Y.A.: An approach to text classification using dimensionality reduction and combination of classifiers. In: Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, IRI 2004, pp. 564–569. IEEE (2004)

    Google Scholar 

  17. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  18. Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. J. Doc. 28(1), 11–21 (1972)

    Article  Google Scholar 

  19. Karasová, V., Krisp, J.M., Virrantaus, K.: Application of spatial association rules for improvement of a risk model for fire and rescue services. In: Proceedings of ScanGIS 2005 (2005)

    Google Scholar 

  20. Lee, D.H., Jeng, S.T., Chandrasekar, P.: Applying data mining techniques for traffic incident analysis. J. Inst. Eng. 44(2), 90 (2004). Singapore

    Google Scholar 

  21. Liang, X.: Government crisis communication on the microblog: a theory framework and the case of Shanghai metro rear-end collision. In: Proceedings of 6th International Conference on Theory and Practice of Electronic Governance, ICEGOV 2012, pp. 248–257. ACM (2012)

    Google Scholar 

  22. Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Financ. 66(1), 35–65 (2011)

    Article  Google Scholar 

  23. Lyall, S.: British Tabloid Hacked Missing Girl’s Voice Mail, Lawyer Says (2011). http://www.nytimes.com/2011/07/05/world/europe/05britain.html

  24. MacLaggan, C.: Exclusive: Livestrong Cancer Charity Drops Lance Armstrong Name from Title (2012). http://tinyurl.com/zrx9slu

  25. Miller, D., Schwartz, R., Weischedel, R., Stone, R.: Named entity extraction from broadcast news. In: Proceedings of DARPA Broadcast News Workshop, pp. 37–40 (1999)

    Google Scholar 

  26. Nielsen, F.: AFINN (2011). http://www2.imm.dtu.dk/pubdb/p.php?6010

  27. Nielsen, F.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint (2011). arXiv:1103.2903

  28. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  29. Papamichail, K.N., French, S.: Design and evaluation of an intelligent decision support system for nuclear emergencies. Decis. Support Syst. 41(1), 84–111 (2005)

    Article  Google Scholar 

  30. Peng, Y., Zhang, Y., Tang, Y., Li, S.: An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decis. Support Syst. 51(2), 316–327 (2011)

    Article  Google Scholar 

  31. Perlroth, N.: Target’s Nightmare Goes On: Encrypted PIN Data Stolen (2013). http://bits.blogs.nytimes.com/2013/12/27/targets-nightmare-goes-on-encrypted-pin-data-stolen

  32. Framework, P.: Play 2.1 documentation. http://www.playframework.com

  33. Porter, M.F.: An algorithm for suffix stripping. Prog.: Electron. Libr. Inf. Syst. 14(3), 130–137 (1980)

    Article  Google Scholar 

  34. Reuters: Banks Suffer After-Hours On Word of Chase Losses (2012). http://www.nytimes.com/2012/05/11/business/daily-stock-market-activity.html

  35. Rish, I.: An empirical study of the Naive Bayes classifier. In: IJCAI Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22, pp. 41–46 (2001)

    Google Scholar 

  36. Robertson, C.: Search Continues After Oil Rig Blast (2010). http://www.nytimes.com/2010/04/22/us/22rig.html

  37. Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)

    Article  MathSciNet  Google Scholar 

  38. Sanger, D.E.: Loss of the Shuttle: the Overview; Shuttle Breaks Up, 7 Dead (2003). http://www.nytimes.com/2003/02/02/us/loss-of-the-shuttle-the-overview-shuttle-breaks-up-7-dead.html

  39. Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: the Azfin text system. ACM Trans. Inf. Syst. (TOIS) 27(2), 12:1–12:19 (2009)

    Article  Google Scholar 

  40. Schwirtz, M.: Oil Pipeline Ruptures in Arkansas (2013). http://www.nytimes.com/2013/03/31/us/oil-pipeline-ruptures-in-arkansas.html

  41. Seeger, M.W., Sellnow, T.L., Ulmer, R.R.: Communication and Organizational Crisis. Praeger Publishers, Westport (2003)

    Google Scholar 

  42. Takeuchi, K., Collier, N.: Bio-medical entity extraction using support vector machines. Artif. Intell. Med. 33(2), 125–137 (2005)

    Article  Google Scholar 

  43. The New York Times: New York Times Article Archive (2014). http://www.nytimes.com/ref/membercenter/nytarchive.html

  44. Wan, X.: Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 553–561. Association for Computational Linguistics (2008)

    Google Scholar 

  45. Youn, S., McLeod, D.: A comparative study for email classification. In: Elleithy, K. (ed.) Advances and Innovations in Systems, Computing Sciences and Software Engineering, pp. 387–391. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Central Michigan University, Mt Pleasant, MI, 48858, USA

    David Kaczynski, Lisa Gandy & Gongzhu Hu

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  1. David Kaczynski
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  2. Lisa Gandy
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  3. Gongzhu Hu
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Corresponding author

Correspondence to Lisa Gandy .

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Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Kaczynski, D., Gandy, L., Hu, G. (2016). Innovations in News Media: Crisis Classification System. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-41561-1_10

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  • Online ISBN: 978-3-319-41561-1

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