Using Sociolinguistic Inspired Features for Gender Classification of Web Authors

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9302)


In this article we present a methodology for classification of text from web authors, using sociolinguistic inspired text features. The proposed methodology uses a baseline text mining based feature set, which is combined with text features that quantify results from theoretical and sociolinguistic studies. Two combination approaches were evaluated and the evaluation results indicated a significant improvement in both combination cases. For the best performing combination approach the accuracy was 84.36%, in terms of percentage of correctly classified web posts.


text classification algorithms sociolinguistics gender identification 


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  1. 1.
    Archakis, A., Kondyli, M.: Introduction to sociolinguistic issues. Nisos(in Greek), Athens (2004)Google Scholar
  2. 2.
    Cheng, N., Chandramouli, R., Subbalakshmi, K.P.: Author gender identification from text. The International Journal of Digital Forensics & Incident. Response 8(1), 78–88 (2011)Google Scholar
  3. 3.
    Soler, J., Wanner, L.: How to Use Less Features and Reach Better Performance in Author Gender Identification. Proceedings of LREC 2014 (2014)Google Scholar
  4. 4.
    Koppel, M., Argamon, S., Shimoni, A.R.: Automatically categorizing written texts by author gender. Literary and Linguistic Computing 17(4), 401–412 (2002)CrossRefGoogle Scholar
  5. 5.
    Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Mining the Blogosphere: Age, gender and the varieties of self-expression. First Monday 12(9) (September 2007)DOI=Http:// Scholar
  6. 6.
    Ansari, Y.Z., Azad, S.A., Akhtar, H.: Gender Classification of Blog Authors. In: International Journal of Sustainable Development and Green Economic. Volume 2. (2013) ISSN no.:2315–4721Google Scholar
  7. 7.
    Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on Twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP ’11), Stroudsburg, PA, USA, Association for Computational Linguistics (2011) 1301–1309Google Scholar
  8. 8.
    Kobayashi, D., Matsumura, N., Ishizuka, M.: Automatic Estimation of Bloggers’ Gender. In: Proceedings of International Conference on Weblogs and Social Media, Boulder: Omnipress (2007)Google Scholar
  9. 9.
    Zhang, C., Zhang, P.: Predicting gender from blog posts. Technical report, University of Massachusetts Amherst, USA (2010)Google Scholar
  10. 10.
    Mukherjee, A., Liu, B.: Improving gender classification of blog authors. In: Proceedings of the 2010 conference on Empirical Methods in natural Language Processing (EMNLP’10). (2010) 207–217 DOI=http:/ Scholar
  11. 11.
    Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting Age and Gender in Online Social Networks. In: Proceedings of the \(3^{rd}\) International Workshop on Search and Mining User-Generated Contents (SMUC’11), Glasgow, UK (2011) 37–44Google Scholar
  12. 12.
    Sarawgi, R., Gajulapalli, K., Choi, Y.: Gender Attribution: Tracing Stylometric Evidence Beyond Topic and Genre. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning (Portland, USA, 9 - 24 June, 2011), Stroudsburg, PA, USA, Association for Computational Linguistics (2011) 78–86Google Scholar
  13. 13.
    Holmgren, J., Shyu, E.: Gender classification of facebook posts. (2013)Google Scholar
  14. 14.
    Rangel, F., Rosso, P.: Use of Language and Author Profiling: Identification of Gender and Age. In: Proceedings of the Tenth International Workshop on Natural Language Processing and Cognitive Science, Marseille, France (October 2013)Google Scholar
  15. 15.
    Marquardt, J., Farnadi, G., Vasudevan, G., Moens, M.F., Davalos, S., Teredesai, A., De Cock, M.: Age and Gender Identification in Social Media. Proceedings of CLEF 2014 Evaluation Labs (2014)Google Scholar
  16. 16.
    Gordon, E.: Sex, speech, and stereotypes: Why women use prestige speech forms more than men. Language in society 26(1), 47–63 (1997)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Cameron, D.: Gender, Language, and Discourse: A Review Essay. Signs: Journal of women, Culture and Society 23(4) (1998) 945–973Google Scholar
  18. 18.
    Cameron, D.: Language, Gender, and Sexuality: Current Issues and New Directions. Applied linguistics 26(4) (2005) 482–502 DOI=10.1093/applin/ami027Google Scholar
  19. 19.
    Bucholtz, M.: You da man: Narrating the racial other in the production of white masculinity. Journal of sociolinguistics 3(4), 443–460 (1999)CrossRefGoogle Scholar
  20. 20.
    Bucholtz, M., Liang, A.C., Sutton, L.A.: Reinventing identities: The gendered self in discourse. Oxford University Press, New York (1999)Google Scholar
  21. 21.
    Fishman, P.M.: Interaction: The work women do. In: Language, Gender and Society, Rowley, Mass.: Newbury House (1983) 89–102Google Scholar
  22. 22.
    Lakoff, R.: Talking Power: The Politics of Language. Basic Books, New York (1990)Google Scholar
  23. 23.
    Lakoff, R.: Language and Women’s Place. Harper and Row, New York (1975)zbMATHGoogle Scholar
  24. 24.
    Alami, M., Sabbah, M., Iranmanesh, M.: Male-Female Discourse Difference in Terms of Lexical Density. Research Journal Of Applied Sciences, Engineering and Technology. 5, 5365–5369 (2013)Google Scholar
  25. 25.
    Eckert, P.: Three waves of variation study: The emergence of meaning in the study of sociolinguistic variation. Annual review of Anthropology. 41, 87–100 (2012)CrossRefGoogle Scholar
  26. 26.
    Moore, E., Podesva, R.: Style, indexicality, and the social meaning of tag questions. In: Language in Society. Volume 38, Cambridge Univ Press (2009) 447–485Google Scholar
  27. 27.
    Bucholtz, M.: From ’Sex Differences’ to Gender Variation in Sociolinguistics. In: University of Pennsylvania Working Papers in Linguistics (Papers from NWAV 30). Volume 8, University of Pennsylvania, Department of Linguistics (2002) 33–45Google Scholar
  28. 28.
    Bucholtz, M.: Theories of Discourse as Theories of Gender: Discourse Analysis in Language and Gender Studies. In: The Handbook of Language and Gender, Oxford Blackwell (2003) 43–68Google Scholar
  29. 29.
    Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: Writing-style features and classification techniques. Journal of the American Society for Information Science and Technology 57(3), 378–393 (2006)CrossRefGoogle Scholar
  30. 30.
    Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of Language Resources and Evaluation (LREC). 6, 417–422 (2006)Google Scholar
  31. 31.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Elsevier, Morgan-Kaufman Series of Data Management Systems), San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Multidimentional Data Analysis and Knowledge Management Laboratory, Dept. of Computer Engineering and InformaticsUniversity of Patras26500-RionGreece

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