Revealing Word Order: Using Serial Position in Binomials to Predict Properties of the Speaker
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Three studies test the link between word order in binomials and psychological and demographic characteristics of a speaker. While linguists have already suggested that psychological, cultural and societal factors are important in choosing word order in binomials, the vast majority of relevant research was focused on general factors and on broadly shared cultural conventions. In contrast, in this work we are interested in what word order can tell us about the particular speaker. More specifically, we test the degree to which word order is affected by factors such as gender, race, geographic location, religion, political orientation, and consumer preferences. Using a variety of methodologies and different data sources, we find converging evidence that word order is linked to a broad set of features associated with the speaker. We discuss the theoretical implications of these findings and the potential to use word order as a tool for analyzing large text corpora and data on the web.
KeywordsWord order Binomials Subjective distance On-line data Automated text analysis
We are thankful to Doug Medin, Sonya Sachdeva, bethany ojalehto and Veronica Gerassimova for suggestions and comments on the paper. This work was supported by a DRMS Grant—NSF SES 0962185.
- Argamon, S., Koppel, M., Fine, J., & Shimoni, A. (2003). Gender, genre, and writing style in formal written texts. Text, 23(3), 321–346.Google Scholar
- Brown, R. (1986). Social psychology: The second edition. New York: Macmillan.Google Scholar
- Cooper, W., & Ross, J. (1975). World order. In R. E. Grossman, L. J. San, & T. J. Vance (Eds.), Papers from the parasession on functionalism (pp. 63–111). Chicago: Chicago Linguistic Society.Google Scholar
- Dehghani, M., Gratch, J., Sachdeva, S., & Sagae, K. (2011). Analyzing conservative and liberal blogs related to the construction of the ‘Ground Zero Mosque’. In L. Carlson (Ed.), Proceedings of the 33th annual conference of the Cognitive Science Society, Boston, MA (pp. 1853–1858).Google Scholar
- Inagaki, K., & Hatano, G. (2002). Young children’s thinking about the biological world. New York: Psychology Press.Google Scholar
- Liu, B. (2010). Sentiment analysis and subjectivity. In N. Indurkhya & F. J. Damerau (Eds.), Handbook of natural language processing (2nd ed., pp. 627–666). Boca Raton, FL: Taylor and Francis.Google Scholar
- MacWhinney, B., & Bates, E. (Eds.). (1989). The crosslinguistic study of sentence processing. New York: Cambridge University Press.Google Scholar
- Matlin, M., & Stang, D. J. (1978). The Pollyanna principle: Selectivity in language, memory, and thought. Cambridge, MA: Schenkman.Google Scholar
- Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The\_Google\_Books\_Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M., & Lieberman-Aiden,E. (2011). Quantitative analysis of culture using millions of digitized books. Science, 331(6014), 176–182.Google Scholar
- Mukherjee, A., & Liu, B. (2010). Improving gender classification of blog authors. In Proceedings of the 2010 conference on empirical methods in natural language processing (pp. 207–217). Cambridge, MA: Association for Computational Linguistics.Google Scholar
- Sagi, E., Kaufmann, S., & Clark, B. (2009). Semantic density analysis: comparing word meaning across time and phonetic space. In R. Basili & M. Pennacchiotti (Eds.), Proceedings of the EACL 2009 Workshop on GEMS: Geometrical Models of Natural Language Semantics, Greece, Athens (pp. 104–111).Google Scholar