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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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Abstract

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.

Notes

  1. 1.

    The words for each category are available from the resource available in the “Values Lexicon” section at http://nlp.eecs.umich.edu/downloads.html.

  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.

    https://www.mturk.com.

  4. 4.

    https://www.reddit.com.

  5. 5.

    https://www.blogger.com.

  6. 6.

    Based on estimations provided at https://en.wikipedia.org/wiki/List_of_countries_by_English-speaking_population.

  7. 7.

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

  8. 8.

    We collected these using code from https://github.com/costaspappus/Blogs-Scraper.

  9. 9.

    We use https://www.crummy.com/software/BeautifulSoup/ to clean the HTML.

  10. 10.

    Using https://github.com/saffsd/langid.py.

  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 http://www.worldvaluessurvey.org/.

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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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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. https://doi.org/10.1007/978-3-030-34971-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-34971-4_10

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