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Recent Advances in Computational Intelligence in Defense and Security pp 491–517Cite as

Text Mining in Social Media for Security Threats

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

We discuss techniques for information extraction from texts, and present two applications that use these techniques. We focus in particular on social media texts (Twitter messages), which present challenges for the information extraction techniques because they are noisy and short. The first application is extracting the locations mentioned in Twitter messages, and the second one is detecting the location of the users based on all the tweets written by each user. The same techniques can be used for extracting other kinds of information from social media texts, with the purpose of monitoring the topics, events, emotions, or locations of interest to security and defence applications.

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Notes

  1. 1.

    [25] recently released a dataset of various kinds of social media data annotated with generic location expressions, but not with cities, states/provinces, and countries.

  2. 2.

    https://dev.twitter.com.

  3. 3.

    http://www.geonames.org.

  4. 4.

    The number of countries is larger than 200 because alternative names are counted; the same for states/provinces and cities.

  5. 5.

    https://github.com/rex911/locdet.

  6. 6.

    http://www.ark.cs.cmu.edu/GeoTwitter.

  7. 7.

    https://github.com/utcompling/textgrounder/wiki/RollerEtAl_EMNLP2012.

  8. 8.

    Explained in Sect. 5.2.

  9. 9.

    Not all of these 5000 n-grams are necessarily good location indicators, we don’t manually distinguish them; a machine learning model after training should be able to do so.

  10. 10.

    Alternatively, we also tried the loss function defined as the average squared error of output numbers, which is equivalent to the average Euclidean distance between the estimated location and the true location; this alternative model did not perform well.

  11. 11.

    http://www.mapquest.com.

  12. 12.

    http://www.census.gov/geo/maps-data/maps/pdfs/reference/us_regdiv.pdf.

  13. 13.

    Our code is available at https://github.com/rex911/usrloc.

  14. 14.

    We are unable to conduct t-tests on the Eisenstein models, because of the unavailability of the details of the results produced by these models.

  15. 15.

    We are unable to conduct t-tests on the other models, because of the unavailability of the details of the results produced by these models.

  16. 16.

    Only this metric was reported by the author in the top 3 % features configuration.

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Acknowledgments

I would like to thank my collaborators who helped with the location detection project: Ji Rex Liu, Diman Ghazi, Atefeh Farzindar and Farzaneh Kazemi for task 1 and Ji Rex Liu for task 2.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, Ottawa, ON, K1N 6N5, Canada

    Diana Inkpen

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  1. Diana Inkpen
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Correspondence to Diana Inkpen .

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Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

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Inkpen, D. (2016). Text Mining in Social Media for Security Threats. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-26450-9_19

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