Advertisement

Event Detection from Blogs Using Large Scale Analysis of Metaphorical Usage

  • Brian J. GoodeEmail author
  • Juan Ignacio Reyes M.
  • Daniela R. Pardo-Yepez
  • Gabriel L. Canale
  • Richard M. Tong
  • David Mares
  • Michael Roan
  • Naren Ramakrishnan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)

Abstract

Metaphors shape the way people think, decide, and act. We hypothesize that large-scale variations in metaphor usage in blogs can be used as an indicator of societal events. To this end, we use metaphor analysis on a massive scale to study blogs in Latin America over a period ranging from 2000–2015, with most of our data occurring within a nine-year period. Using co-clustering, we form groups of similar behaving metaphors for Argentina, Ecuador, Mexico, and Venezuela and characterize overrepresented as well as underrepresented metaphors for specific locations. We then focus on the metaphor’s potential relation to events by studying the tobacco tax increase in Mexico from 2009–2011. We study correspondences between changes in metaphor frequency with event occurrences, as well as the effect of temporal scaling of data windows on the frequency relationship between metaphors and events.

Keywords

Metaphors Blogs Open source indicators Event detection 

Notes

Acknowledgments

Supported by the Intelligence Advanced Research Projects Activity (IARPA) via DoI/NBC contract number D12PC000337, the US Government is authorized to reproduce and distribute reprints of this work for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the US Government.

References

  1. 1.
    Bracewell, D.B., Tomlinson, M.T., Mohler, M., Rink, B.: A tiered approach to the recognition of metaphor. In: Gelbukh, A. (ed.) CICLing 2014, Part I. LNCS, vol. 8403, pp. 403–414. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  2. 2.
    Carroll, J.M., Mack, R.L.: Metaphor, computing systems, and active learning. Int. J. Hum.-Comput. Stud. 51(2), 385–403 (1999)CrossRefGoogle Scholar
  3. 3.
    Chakraborty, P., et al.: Forecasting a moving target: Ensemble models for ILI case count predictions. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 262–270 (2014)Google Scholar
  4. 4.
    Geary, J.: I Is an Other: The Secret Life of Metaphor and How it Shapes the Way We See the World. HarperCollins Publishers, New York (2011)Google Scholar
  5. 5.
    Gibbs Jr., R.W.: The real complexities of psycholinguistic research on metaphor. Lang. Sci. 40, 45–52 (2013)CrossRefGoogle Scholar
  6. 6.
    Golshaie, R., Golfam, A.: Processing conventional conceptual metaphors in persian: A corpus-based psycholinguistic study. J. Psycholinguist. Res. 44(5), 495–518 (2014)CrossRefGoogle Scholar
  7. 7.
    Johnson, M.: Metaphorical reasoning. South. J. Philos. 21(3), 371–389 (1983)CrossRefGoogle Scholar
  8. 8.
    Keysar, B., Shen, Y., Glucksberg, S., Horton, W.S.: Conventional language: How metaphorical is it? J. Mem. Lang. 43(4), 576–593 (2000)CrossRefGoogle Scholar
  9. 9.
    Kluger, Y., Basri, R., Chang, J.T., Gerstein, M.: Spectral biclustering of microarray data: Coclustering genes and conditions. Genome Res. 13(4), 703–716 (2003)CrossRefGoogle Scholar
  10. 10.
    Lakoff, G., Johnson, M.: Metaphors We Live By. University of Chicago Press, Chicago (2008)Google Scholar
  11. 11.
    Muthiah, S., et al.: Planned protest modeling in news and social media. In: Innovative Applications of Artificial Intelligence (2015)Google Scholar
  12. 12.
    Neuman, Y., et al.: Metaphor identification in large texts corpora. PLoS ONE 8(4), e62343 (2013)CrossRefGoogle Scholar
  13. 13.
    Ramakrishnan, N., et al.: ‘Beating the News’ with EMBERS: Forecasting civil unrest using open source indicators. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 1799–1808. ACM, New York (2014)Google Scholar
  14. 14.
    Thibodeau, P.H., Boroditsky, L.: Metaphors we think with: The role of metaphor in reasoning. PLoS ONE 6(2), e16782 (2011)CrossRefGoogle Scholar
  15. 15.
    Thibodeau, P.H., Boroditsky, L.: Natural language metaphors covertly influence reasoning. PLoS ONE 8(1), e52961 (2013)CrossRefGoogle Scholar
  16. 16.
    Thibodeau, P.H., Boroditsky, L.: Measuring effects of metaphor in a dynamic opinion landscape. PLoS ONE 10(7), e0133939 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Brian J. Goode
    • 1
    • 2
    Email author
  • Juan Ignacio Reyes M.
    • 3
  • Daniela R. Pardo-Yepez
    • 3
  • Gabriel L. Canale
    • 3
  • Richard M. Tong
    • 4
  • David Mares
    • 3
  • Michael Roan
    • 2
  • Naren Ramakrishnan
    • 1
  1. 1.Discovery Analytics Center (Department of Computer Science)Virginia TechArlingtonUSA
  2. 2.Department of Mechanical EngineeringVirginia TechBlacksburgUSA
  3. 3.Center for Iberian and Latin American Studies (CILAS)UC San DiegoLa JollaUSA
  4. 4.Tarragon Consulting CorporationBerkeleyUSA

Personalised recommendations