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.
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Notes
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Target and source concepts will be represented in all caps: e.g., ELECTIONS.
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Argentina, Ecuador, and Venezuela are not shown to conserve space. Qualitative results from these countries are discussed in terms of their similarities to our Mexico dataset. Differences are explicitly highlighted in Table 2.
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\(\chi ^2\) test (\(\varGamma = 365\)), \(p < 0.001\) for all targets in all countries.
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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.
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Goode, B.J. et al. (2016). Event Detection from Blogs Using Large Scale Analysis of Metaphorical Usage. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_21
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DOI: https://doi.org/10.1007/978-3-319-39931-7_21
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