Skip to main content
SpringerLink
Log in

Perceptions of Social Roles Across Cultures

  • Conference paper
  • First Online:
Social Informatics (SocInfo 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11864))

Included in the following conference series:

Abstract

In this paper we introduce a data set of social roles and their aspects (descriptors or actions) as emerging from surveys conducted across a sample of over 400 respondents from two different cultures: US and India. The responses show that there are indeed differences of role perceptions across the cultures, with actions showcasing less variability, and descriptors exhibiting stronger differences. In addition, we notice strong shifts in sentiment and emotions across the cultures. We further present a pilot study in predicting social roles based on attributes by leveraging dependency-based corpus statistics and embedding models. Our evaluations show that models trained on the same culture as the test set are better predictors of social role ranking.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    English is one of the official languages of India and the second most-spoken language behind Hindi.

  2. 2.

    Normal sentences are rarely this long, and upon manual inspection we found that these tend to be malformed sentences.

  3. 3.

    https://github.com/rfk/pyenchant.

  4. 4.

    Results for word association tasks are traditionally low, and our results are within the same range as previous word association research [21].

References

  1. Andrews, M., Vigliocco, G., Vinson, D.: Integrating experiential and distributional data to learn semantic representations. Psychol. Rev. 116(3), 463 (2009)

    Article  Google Scholar 

  2. Ashforth, B.E., Kreiner, G.E., Fugate, M.: All in a day’s work: boundaries and micro role transitions. Acad. Manag. Rev. 25(3), 472–491 (2000)

    Article  Google Scholar 

  3. Bamman, D., Dyer, C., Smith, N.A.: Distributed representations of geographically situated language. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 828–834 (2014). http://www.aclweb.org/anthology/P/P14/P14-2134

  4. Bamman, D., O’Connor, B., Smith, N.A.: Learning latent personas of film characters, pp. 352–361. http://www.aclweb.org/anthology/P13-1035

  5. Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)

    MATH  Google Scholar 

  6. Berinsky, A.J., Huber, G.A., Lenz, G.S.: Evaluating online labor markets for experimental research: Amazon.com’s mechanical turk. Political Anal. 20(3), 351–368 (2012)

    Article  Google Scholar 

  7. Biddle, B.J.: Recent developments in role theory. Ann. Rev. Sociol. 12(1), 67–92 (1986)

    Article  Google Scholar 

  8. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  9. Brown, P.F., Desouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Computat. Linguist. 18(4), 467–479 (1992)

    Google Scholar 

  10. Bruni, E., Boleda, G., Baroni, M., Tran, N.K.: Distributional semantics in technicolor. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pp. 136–145. Association for Computational Linguistics (2012)

    Google Scholar 

  11. Chaudhari, D.L., Damani, O.P., Laxman, S.: Lexical co-occurrence, statistical significance, and word association. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1058–1068. Association for Computational Linguistics (2011)

    Google Scholar 

  12. Cialdini, R.B., Kallgren, C.A., Reno, R.R.: A focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior (1991)

    Google Scholar 

  13. Clifford, S., Jewell, R.M., Waggoner, P.D.: Are samples drawn from mechanical turk valid for research on political ideology? Res. Polit. 2(4), 2053168015622072 (2015)

    Google Scholar 

  14. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)

    Google Scholar 

  15. Daga, S.S., Raval, V.V., Raj, S.P.: Maternal meta-emotion and child socioemotional functioning in immigrant Indian and white American families. Asian Am. J. Psychol. 6(3), 233 (2015)

    Article  Google Scholar 

  16. De Deyne, S., Navarro, D.J., Storms, G.: Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behav. Res. Methods 45(2), 480–498 (2013)

    Article  Google Scholar 

  17. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)

    Article  Google Scholar 

  18. Eagly, A.H., Karau, S.J.: Role congruity theory of prejudice toward female leaders. Psychol. Rev. 109(3), 573 (2002)

    Article  Google Scholar 

  19. Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), Genova, IT (2006)

    Google Scholar 

  20. Feng, Y., Lapata, M.: Visual information in semantic representation. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 91–99. Association for Computational Linguistics (2010)

    Google Scholar 

  21. Garimella, A., Banea, C., Mihalcea, R.: Demographic-aware word associations. In: Proceedings of the International Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark (2017)

    Google Scholar 

  22. Garimella, A., Mihalcea, R., Pennebaker, J.: Identifying cross-cultural differences in word usage. In: Proceedings of the International Conference on Computational Linguistics (COLING 2016), Japan (2016)

    Google Scholar 

  23. Gupta, A., Boleda, G., Baroni, M., Padó, S.: Distributional vectors encode referential attributes. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 12–21. Association for Computational Linguistics, Lisbon, September 2015. https://doi.org/10.18653/v1/D15-1002. https://www.aclweb.org/anthology/D15-1002

  24. Hovy, D., Purschke, C.: Capturing regional variation with distributed place representations and geographic retrofitting. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4383–4394 (2018)

    Google Scholar 

  25. Jurgens, D., Tsvetkov, Y., Jurafsky, D.: Writer profiling without the writer’s text. In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10540, pp. 537–558. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67256-4_43

    Chapter  Google Scholar 

  26. Katz, D., Kahn, R.L.: The Social Psychology of Organizations, vol. 2. Wiley, New York (1978)

    Google Scholar 

  27. Kent, G.H., Rosanoff, A.J.: A study of association in insanity. Am. J. Psychiatry 67(1), 37–96 (1910)

    Article  Google Scholar 

  28. Levy, O., Goldberg, Y.: Dependency-based word embeddings, pp. 302–308 (2014). http://www.aclweb.org/anthology/P14-2050

  29. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014). http://www.aclweb.org/anthology/P/P14/P14-5010

  30. Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL HLT, Atlanta, GA, USA, pp. 746–751 (2013)

    Google Scholar 

  31. Miller, G.A.: WordNet: a lexical database for English. Commun. Assoc. Comput. Mach. 38(11), 39–41 (1995)

    Google Scholar 

  32. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  33. Nelson, D.L., McEvoy, C.L., Schreiber, T.A.: The university of South Florida free association, rhyme, and word fragment norms. Behav. Res. Methods Instrum. Comput. 36(3), 402–407 (2004)

    Article  Google Scholar 

  34. Paul, M., Girju, R.: Cross-cultural analysis of blogs and forums with mixed-collection topic models. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, pp. 1408–1417, August 2009. http://www.aclweb.org/anthology/D/D09/D09-1146

  35. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  36. Plutchik, R.: The Emotions. Random House, New York (1962)

    Google Scholar 

  37. Raval, V.V., Raval, P.H., Salvina, J.M., Wilson, S.L., Writer, S.: Mothers’ socialization of children’s emotion in india and the usa: a cross-and within-culture comparison. Soc. Dev. 22(3), 467–484 (2013)

    Article  Google Scholar 

  38. Ritzer, G., et al.: The McDonaldization of Society. Pine Forge Press, Newbury Park (1992)

    Google Scholar 

  39. Rogers, A., Hosur Ananthakrishna, S., Rumshisky, A.: What’s in your embedding, and how it predicts task performance. In: Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA, pp. 2690–2703, August 2018

    Google Scholar 

  40. Roseman, I.J., Dhawan, N., Rettek, S.I., Naidu, R., Thapa, K.: Cultural differences and cross-cultural similarities in appraisals and emotional responses. J. Cross Cult. Psychol. 26(1), 23–38 (1995)

    Article  Google Scholar 

  41. Salton, G., Lesk, M.: Computer evaluation of indexing and text processing. J. ACM 15(1), 8–36 (1968). https://doi.org/10.1145/321439.321441. http://portal.acm.org/citation.cfm?doid=321439.321441

    Article  MATH  Google Scholar 

  42. Shweder, R.A.: Thinking Through Cultures: Expeditions in Cultural Psychology. Harvard University Press, Cambridge (1991)

    Google Scholar 

  43. Sunstein, C.R.: Social norms and social roles. Columbia Law Rev. 96(4), 903–968 (1996)

    Article  Google Scholar 

  44. Triandis, H.C., Marin, G., Hui, C.H., Lisansky, J., Ottati, V.: Role perceptions of hispanic young adults. J. Cross Cult. Psychol. 15(3), 297–320 (1984)

    Article  Google Scholar 

  45. Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010)

    Google Scholar 

  46. Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94 (2012). http://dl.acm.org/citation.cfm?id=2390688

  47. Wilson, T., et al.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP 2005 Interactive Demonstrations (2005)

    Google Scholar 

  48. Zafar, L., Afzal, M.T., Ahmed, U.: Exploiting polarity features for developing sentiment analysis tool. In: EMSASW@ ESWC (2017)

    Google Scholar 

  49. Zou, W.Y., Socher, R., Cer, D., Manning, C.D.: Bilingual word embeddings for phrase-based machine translation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1393–1398 (2013)

    Google Scholar 

Download references

Acknowledgments

This material is based in part upon work supported by the Michigan Institute for Data Science, by the National Science Foundation (grant #1815291), and by the John Templeton Foundation (grant #61156). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the Michigan Institute for Data Science, the National Science Foundation, or the John Templeton Foundation.

Author information

Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    MeiXing Dong, David Jurgens, Carmen Banea & Rada Mihalcea

Authors
  1. MeiXing Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Jurgens
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carmen Banea
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rada Mihalcea
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to MeiXing Dong .

Editor information

Editors and Affiliations

  1. Qatar Computing Research Institute, Doha, Qatar

    Ingmar Weber

  2. Qatar Computing Research Institute, Doha, Qatar

    Kareem M. Darwish

  3. University of Koblenz-Landau, Koblenz, Germany

    Claudia Wagner

  4. Max Planck Institute for Demographic Research, Rostock, Germany

    Emilio Zagheni

  5. Northeastern University, Boston, MA, USA

    Laura Nelson

  6. Max Planck Institute for Demographic Research, Rostock, Germany

    Samin Aref

  7. GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany

    Fabian Flöck

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dong, M., Jurgens, D., Banea, C., Mihalcea, R. (2019). Perceptions of Social Roles Across Cultures. In: Weber, I., et al. Social Informatics. SocInfo 2019. Lecture Notes in Computer Science(), vol 11864. Springer, Cham. https://doi.org/10.1007/978-3-030-34971-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34971-4_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34970-7

  • Online ISBN: 978-3-030-34971-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation