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.
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Notes
- 1.
English is one of the official languages of India and the second most-spoken language behind Hindi.
- 2.
Normal sentences are rarely this long, and upon manual inspection we found that these tend to be malformed sentences.
- 3.
- 4.
Results for word association tasks are traditionally low, and our results are within the same range as previous word association research [21].
References
Andrews, M., Vigliocco, G., Vinson, D.: Integrating experiential and distributional data to learn semantic representations. Psychol. Rev. 116(3), 463 (2009)
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)
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
Bamman, D., O’Connor, B., Smith, N.A.: Learning latent personas of film characters, pp. 352–361. http://www.aclweb.org/anthology/P13-1035
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)
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)
Biddle, B.J.: Recent developments in role theory. Ann. Rev. Sociol. 12(1), 67–92 (1986)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Eagly, A.H., Karau, S.J.: Role congruity theory of prejudice toward female leaders. Psychol. Rev. 109(3), 573 (2002)
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)
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)
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)
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)
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
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)
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
Katz, D., Kahn, R.L.: The Social Psychology of Organizations, vol. 2. Wiley, New York (1978)
Kent, G.H., Rosanoff, A.J.: A study of association in insanity. Am. J. Psychiatry 67(1), 37–96 (1910)
Levy, O., Goldberg, Y.: Dependency-based word embeddings, pp. 302–308 (2014). http://www.aclweb.org/anthology/P14-2050
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
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL HLT, Atlanta, GA, USA, pp. 746–751 (2013)
Miller, G.A.: WordNet: a lexical database for English. Commun. Assoc. Comput. Mach. 38(11), 39–41 (1995)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
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)
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
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)
Plutchik, R.: The Emotions. Random House, New York (1962)
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)
Ritzer, G., et al.: The McDonaldization of Society. Pine Forge Press, Newbury Park (1992)
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
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)
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
Shweder, R.A.: Thinking Through Cultures: Expeditions in Cultural Psychology. Harvard University Press, Cambridge (1991)
Sunstein, C.R.: Social norms and social roles. Columbia Law Rev. 96(4), 903–968 (1996)
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)
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)
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
Wilson, T., et al.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP 2005 Interactive Demonstrations (2005)
Zafar, L., Afzal, M.T., Ahmed, U.: Exploiting polarity features for developing sentiment analysis tool. In: EMSASW@ ESWC (2017)
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)
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.
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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
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