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On Utilizing Nonstandard Abbreviations and Lexicon to Infer Demographic Attributes of Twitter Users

  • Nathaniel MoselyEmail author
  • Cecilia Ovesdotter Alm
  • Manjeet Rege
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 346)

Abstract

Automatically determining demographic attributes of writers with high accuracy, based on their texts, can be useful for a range of application domains, including smart ad placement, security, the discovery of predator behaviors, enabling automatic enhancement of participants’ profiles for extended analysis, and various other applications. It is also of interest from the perspective to linguists who may wish to build on such inference for further sociolinguistic analysis. Previous work indicates that attributes such as author gender can be determined with some amount of success, using various methods, such as analysis of shallow linguistic patterns or topic, in authors’ written texts. Author age appears more difficult to determine, but previous research has been somewhat successful at classifying age as a binary (e.g. over or under 30), ternary, or even as a continuous variable using various techniques. In this work, we show that word and phrase abbreviation patterns can be used toward determining user age using novel binning, as well as toward determining binary user gender, and ternary user education level. Notable results include age classification accuracy of up to 83% (67% above relative majority class baseline) using a support vector machine classifier and PCA extracted features, including n-grams. User ages were classified into 10 equally sized age bins and achieved 51% accuracy (34% above baseline) when using only abbreviation features. Gender classification achieved 75% accuracy (13% above baseline) using only abbreviation features, PCA extracted, and education classification achieved 62% accuracy, 19% above baseline with PCA extracted abbreviation features. Also presented is an analysis of the evident change in author abbreviation use over time on Twitter.

Keywords

Age Prediction Age Binning Gender Prediction Education Prediction Tweet Abbreviations and Lexical Ngrams 

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References

  1. 1.
    Mesthrie, R.: Introducing Sociolinguistics. Edinburgh University Press (2009), http://books.google.com/books?id=uy1xbYDsU8kC
  2. 2.
    Han, B., Baldwin, T.: Lexical normalisation of short text messages: Makn sens a #twitter. In: Proceedings of HLT, pp. 368–378 (2011)Google Scholar
  3. 3.
    Rosenthal, S., McKeown, K.: Age prediction in blogs: A study of style, content, and online behavior in pre- and post-social media generations. In: Proceedings of ACL, Portland, OR, USA, vol. 1, pp. 763–772 (2011)Google Scholar
  4. 4.
    Sarawgi, R., Gajulapalli, K., Choi, Y.: Gender attribution: Tracing stylometric evidence beyond topic and genre. In: Proceedings of CoNLL, Portland, Oregon, pp. 78–86 (2011)Google Scholar
  5. 5.
    Derczynski, L., Maynard, D., Aswani, N., Bontcheva, K.: Microblog-genre noise and impact on semantic annotation accuracy. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media, ser. HT 2013, pp. 21–30. ACM, New York (2013)CrossRefGoogle Scholar
  6. 6.
    Ritter, A., Sam, Clark, M., Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, ser. EMNLP 2011, pp. 1524–1534. Association for Computational Linguistics, Stroudsburg (2011)Google Scholar
  7. 7.
    Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.A.: Part-of-speech tagging for Twitter: Annotation, features, and experiments. In: Proceedings of ACL HLT: Short Papers, vol. 2, pp. 42–47 (2011)Google Scholar
  8. 8.
    Kaufmann, M., Kalita, J.: Syntactic normalization of Twitter messages. In: Proceedings of ICON, pp. 149–158 (2010)Google Scholar
  9. 9.
    Gouws, S., Metzler, D., Cai, C., Hovy, E.: Contextual bearing on linguistic variation in social media. In: Proceedings of LSM, pp. 20–29 (2011)Google Scholar
  10. 10.
    Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Unsupervised cleansing of noisy text. In: Proceedings of COLING (2010)Google Scholar
  11. 11.
    Wagner, S.E.: Age grading in sociolinguistic theory. Language and Linguistics Compass 6, 371–382 (2012)CrossRefGoogle Scholar
  12. 12.
    Moseley, N., Alm, D. C.O., Rege, D. M.: A user-annotated microtext data set for modeling and analyzing sociolinguistic characteristics and age grading of Twitter users. In: EMNLP 2013: Conference on Empirical Methods in Natural Language Processing, SIGDAT, Seattle (October 2013)Google Scholar
  13. 13.
    Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on Twitter. In: Proceedings of EMNLP, pp. 1301–1309 (2011)Google Scholar
  14. 14.
    Udani, G.: An exhaustive study of Twitter users across the world. Beevolve Technologies (October 2012), http://www.beevolve.com/twitter-statistics/
  15. 15.
    Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceedings of ETMTNLP (2002)Google Scholar
  16. 16.
    Stolcke, A.: SRILM - an extensible language modeling toolkit. In: Proceedings of 7th International Conference on Spoken Language Processing (2002)Google Scholar
  17. 17.
    Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. Journal of Machine Learning Research 2, 419–444 (2002)zbMATHGoogle Scholar
  18. 18.
    Smith, A., Brenner, J.: Twitter use 2012. Pew Research Centers Internet & American Life Project, Tech. Rep (2012)Google Scholar
  19. 19.
    Nguyen, D., Smith, N.A., Rosé, C.P.: Author age prediction from text using linear regression. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, Portland, Oregon (2011)Google Scholar
  20. 20.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)CrossRefzbMATHGoogle Scholar
  21. 21.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)Google Scholar
  22. 22.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)Google Scholar
  23. 23.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 1–27 (2011)CrossRefGoogle Scholar
  24. 24.
    Turk, M.: Analysis and visualization of multi-scale astrophysical simulations using python and numpy. In: Proceedings of 7th Python in Science Conference (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nathaniel Mosely
    • 1
    Email author
  • Cecilia Ovesdotter Alm
    • 1
  • Manjeet Rege
    • 2
  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.University of St. ThomasSt. PaulUSA

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