Networks and Niches the Continuing Significance of Ethnic Connections

Abstract

Ethnicity is a susceptible problem in modern society. But we need to know about it to understand the population of our society. To solve this problem, we use the surname to tackle the ethnic distribution and linkage of society. We build a surname-ethnicity classification model with Recurrent Neural Network and a large-scale surname dataset of ORBIS. Using this method, we analyze the spatial distribution of ethnicity. And we observe the activation of ethnic linkage in particular situations, especially on international trading. We expect this method to provide new ideas and expand on research that understands the characteristics of the population of society.

References

1. Farrer, L.A., Cupples, L.A., Haines, J.L., et al.: Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and alzheimer disease: a meta-analysis. JAMA. 16, 1349–1356 (2003)

   Google Scholar

2. Winham, S.J., de Andrade, M., Miller, V.M.: Genetics of cardiovascular disease: importance of sex and ethnicity. Atherosclerosis 241, 219–228 (2015)

   Google Scholar

3. Bhopal, K.: Gender, ethnicity and career progression in UK higher education: a case study analysis. Res. Pap. Educ. 1–16 (2019)

   Google Scholar

4. Gillborn, D.: Race, ethnicity and education: teaching and learning in multi-ethnic schools (1990)

   Google Scholar

5. McGowen, R.: The Many Colors of Crime: Inequalities of Race, Ethnicity, and Crime in America. NYU Press (2006)

   Google Scholar

6. Rojas-Gaona, C.E., Hong, J.S., Peguero, A.A.: The significance of race/ethnicity in adolescent violence: a decade of review, 2005–2015. J. Crim. Just. 46, 137–147 (2016)

   CrossRef  Google Scholar

7. Burchard, E.G., Ziv, E., Stable, E.J.P., Sheppard, D.: The importance of race and ethnic background in biomedical research and clinical practice. N. Engl. J. Med. 348, 1170–5 (2003)

   Google Scholar

8. Barr, D.A.: Health Disparities in the United States: Social Class, Race, Ethnicity, and Health (2014)

   Google Scholar

9. Quesada, J., Hart, L.K., Bourgois, P.: Structural vulnerability and health: Latino migrant laborers in the United States. Med. Anthropol. 30(278), 339–362 (2011)

   Google Scholar

10. Lauderdale, D.S., Kestenbaum, B.: Asian American ethnic identification by surname. Popul. Res. Policy Rev. 19, 283–300 (2000)

    Google Scholar

11. Mateos, P.: A review of name-based ethnicity classification methods and their potential in population studies. Popul. Space Place 13, 243–263 (2007)

    Google Scholar

12. Coldman, A.J., et al.: The classification of ethnic status using name information. J. Epidemiol. Community Health 42, 390–395 (1988)

    CrossRef  Google Scholar

13. Ethnic identification on adolescents' evaluations of advertisements: Osei Appiah. J. Advert. Res. 41, 7–22 (2001)

    Google Scholar

14. Chang, J., Rosenn, I., Backstrom, L., Marlow, C.: Ethnicity on social networks. In: ICWSM, vol. 10, pp. 18–25 (2010)

    Google Scholar

15. Ambekar, A., Ward, C., Mohammed, J., Male, S., Skiena, S.: Name-ethnicity classification from open sources. In: ACM SIGKDD, pp. 49–58 (2009)

    Google Scholar

16. Liu, W., Ruths, D.: What's in a name? using first names as features for gender inference in twitter, analyzing microtext. In: AAAI spring symposium (2013)

    Google Scholar

17. Pennacchiotti, M., Popescu, A.-M.: A machine learning approach to twitter user classification. In: ICWSM, vol.11, pp. 281–288 (2011)

    Google Scholar

18. Large-scale diversity estimation through surname origin inference: Antoine mazières and camille roth. Bull. Sociol. Methodol. 139, 59–73 (2018)

    CrossRef  Google Scholar

19. Jun, J., Mizuno, T.: Detecting ethnic spatial distribution of business people using machine learning. Information 11, 197 (2020)

    Google Scholar

20. Jun, J., Mizuno, T.: Detecting ethnic spatial distribution of business people using recurrent neural networks. In: IEEE/WIC/ACM International Conference on Web Intelligence (2019)

    Google Scholar

21. Long short-term memory: Sepp Hochreiter and Jürgen Schmidhuber. Neural Comput. 9, 1735–1780 (1997)

    CrossRef  Google Scholar

22. Kaggle.: 120 years of Olympic history: athletes and results. Kaggle. https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results/ (2020)

23. Lee, J., Hyunjae, K., Ko, M., Choi, D., Choi, J., Kang, J.: Name nationality classification with recurrent neural networks. In: IJCAI, pp. 2081–2087 (2017)

    Google Scholar

24. CIA: the world factbook. CIA. https://www.cia.gov/library/publications/the-world-factbook/ (2020)

25. Wikipedia. https://www.wikipedia.org// (2020)

26. WorldpopulationReview. http://worldpopulationreview.com// (2020)

27. Rosvall, M., Axelsson, D., Bergstrom, C.T.: The map equation. Eur. Phys. J. Spec. Top. 178, 13 (2009)

    Google Scholar

28. Peterson Institute for International Economics.: Trump's Trade War Timeline: An Up-to-Date Guide. PIIE. https://www.piie.com/blogs/trade-investment-policy-watch/trump-trade-war-china-date-guide/ (2020)

29. Besbes, A.: Overview and benchmark of traditional and deep learning models in text classification. https://ahmedbesbes.com/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification.html/ (2020)

30. Census: about race. Census. https://www.census.gov/topics/population/race/about.html/ (2020)

31. Rauch, J.E.V.: Ethnic Chinese networks in international trade. Rev. Econ. Stat. 84, 116–130 (2002)

    CrossRef  Google Scholar

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Authors and Affiliations

1. National Institute of Informatics, Tokyo, Japan

   Joomi Jun & Takayuki Mizuno

Corresponding author

Correspondence to Takayuki Mizuno .

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Editors and Affiliations

1. Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, Kyoto, Japan

   Prof. Yuichi Ikeda

2. Department of Mathematics, Niigata University, Niigata, Nishi-ku, Japan

   Prof. Dr. Hiroshi Iyetomi

3. National Institute of Informatics, Tokyo, Japan

   Prof. Takayuki Mizuno

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Jun, J., Mizuno, T. (2021). Detecting Ethnic Linkages in Economic Networks Using Machine Learning. In: Ikeda, Y., Iyetomi, H., Mizuno, T. (eds) Big Data Analysis on Global Community Formation and Isolation. Springer, Singapore. https://doi.org/10.1007/978-981-15-4944-1_10

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• DOI : https://doi.org/10.1007/978-981-15-4944-1_10

• Published: 13 June 2021

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