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Measuring Personal Values in Cross-Cultural User-Generated Content

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Social Informatics (SocInfo 2019)

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

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There are several standard methods used to measure personal values, including the Schwartz Values Survey and the World Values Survey. While these tools are based on well-established questionnaires, they are expensive to administer at a large scale and rely on respondents to self-report their values rather than observing what people actually choose to write about. We employ a lexicon-based method that can computationally measure personal values on a large scale. Our approach is not limited to word-counting as we explore and evaluate several alternative approaches to quantifying the usage of value-related themes in a given document. We apply our methodology to a large blog dataset comprised of text written by users from different countries around the world in order to quantify cultural differences in the expression of person values on blogs. Additionally, we analyze the relationship between the value themes expressed in blog posts and the values measured for some of the same countries using the World Values Survey.

Y. Shen and S. R. Wilson—Equal contributions

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Change history

  • 11 November 2019

    The original version of this chapter was revised. A missing citation was added and the bibliography was updated accordingly.


  1. 1.

    The words for each category are available from the resource available in the “Values Lexicon” section at

  2. 2.

    While other contextual word embeddings like ELMo [16] do a good job of capturing the meanings of words in specific contexts, lexicons such as the values lexicon that we use is this study do not provide contexts along with the category-specific words, and so further research would be required to determine how to best create, e.g., value-specific dictionary embeddings with ELMo to use within the DDR framework.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

    Based on estimations provided at

  7. 7.

    At least 1,000 users claim to be from that country.

  8. 8.

    We collected these using code from

  9. 9.

    We use to clean the HTML.

  10. 10.


  11. 11.

    As the overall results are not expected to change by a noticeable degree based on our evaluation, we opt not to use the UCRC method in the present analysis.

  12. 12.

    We use data from round 6 of the WVS, available at


  1. Ball-Rokeach, S., Rokeach, M., Grube, J.W.: The Great American Values Test: Influencing Behavior and Belief Through Television. Free Press, New York (1984)

    Google Scholar 

  2. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)

  3. Boyd, R.L., Wilson, S.R., Pennebaker, J.W., Kosinski, M., Stillwell, D.J., Mihalcea, R.: Values in words: using language to evaluate and understand personal values. In: Ninth International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  4. Chung, C.K., Pennebaker, J.W.: Finding values in words: using natural language to detect regional variations in personal concerns. In: Geographical psychology: Exploring the interaction of environment and behavior, pp. 195–216 (2014).

  5. Faulkner, S.L., Baldwin, J.R., Lindsley, S.L., Hecht, M.L.: Layers of meaning: an analysis of definitions of culture. In: Redefining Culture: Perspectives Across the Disciplines, pp. 27–51 (2006)

    Google Scholar 

  6. Garten, J., Hoover, J., Johnson, K.M., Boghrati, R., Iskiwitch, C., Dehghani, M.: Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis. Behav. Res. Methods 50(1), 344–361 (2018)

    Article  Google Scholar 

  7. Graham, J., Haidt, J., Nosek, B.A.: Liberals and conservatives rely on different sets of moral foundations. J. Pers. Soc. Psychol. 96(5), 1029 (2009)

    Article  Google Scholar 

  8. Hofstede, G.: Dimensionalizing cultures: the hofstede model in context. Online Readings Psychol. Cult. 2(1), 8 (2011)

    Article  Google Scholar 

  9. Inglehart, R., et al.: World values survey: round six-country-pooled datafile 2010–2014. JD Systems Institute, Madrid (2014)

    Google Scholar 

  10. Kendall, M.G.: The treatment of ties in ranking problems. Biometrika 33(3), 239–251 (1945)

    Article  MathSciNet  Google Scholar 

  11. Knafo, A., Roccas, S., Sagiv, L.: The value of values in cross-cultural research: a special issue in honor of shalom schwartz (2011)

    Article  Google Scholar 

  12. Landes, S., Leacock, C., Tengi, R.I.: Building semantic concordances. WordNet Electron. Lexical Database 199(216), 199–216 (1998)

    Google Scholar 

  13. Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  14. Miller, G.: WordNet: An Electronic Lexical Database. MIT press, Cambridge (1998)

    MATH  Google Scholar 

  15. Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of liwc2015. Technical report (2015)

    Google Scholar 

  16. Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)

  17. Ramirez-Esparza, N., Chung, C.K., Kacewicz, E., Pennebaker, J.W.: The psychology of word use in depression forums in English and in Spanish: testing two text analytic approaches. In: In the International AAAI Conference on Web and Social Media (2008)

    Google Scholar 

  18. Rohan, M.J.: A rose by any name? the values construct. Pers. Soc. Psychol. Rev. 4(3), 255–277 (2000).

    Article  Google Scholar 

  19. Rokeach, M.: Beliefs, Attitudes, and Values, vol. 34. Jossey-Bass, San Francisco (1968)

    Google Scholar 

  20. Rokeach, M.: The Nature of Human Values, vol. 438. Free press, New York (1973)

    Google Scholar 

  21. Schwartz, S.H.: Universals in the content and structure of values: theoretical advances and empirical tests in 20 countries. Adv. Exp. Soc. Psychol. 25, 1–65 (1992).

    Article  Google Scholar 

  22. Schwartz, S.H.: An overview of the Schwartz theory of basic values. Online Readings Psychol. Cult. 2(1), 11 (2012)

    Article  Google Scholar 

  23. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  24. Wilson, S.R., Shen, Y., Mihalcea, R.: Building and validating hierarchical lexicons with a case study on personal values. In: Staab, S., Koltsova, O., Ignatov, D.I. (eds.) SocInfo 2018. LNCS, vol. 11185, pp. 455–470. Springer, Cham (2018).

    Chapter  Google Scholar 

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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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Shen, Y., Wilson, S.R., Mihalcea, R. (2019). Measuring Personal Values in Cross-Cultural User-Generated Content. In: Weber, I., et al. Social Informatics. SocInfo 2019. Lecture Notes in Computer Science(), vol 11864. Springer, Cham.

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