research-article

Public Access

Nationality Classification Using Name Embeddings

Authors:

Steven SkienaAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 1897 - 1906

https://doi.org/10.1145/3132847.3133008

Published: 06 November 2017 Publication History

Abstract

Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification.

We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems. Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial better than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, fine-grained nationality classifier available.

As a social media application, we apply our classifiers to the followers of major Twitter celebrities over six different domains. We demonstrate stark differences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by different groups. Finally, we identify an anomalous political figure whose presumably inflated following appears largely incapable of reading the language he posts in.

References

[1]

Rami Al-Rfou, Bryan Perozzi, and Steven Skiena. 2013. Polyglot: Distributed word representations for multilingual NLP. ACL (2013).

[2]

Anurag Ambekar, Charles Ward, Jahangir Mohammed, Swapna Male, and Steven Skiena. 2009. Name-ethnicity classification from open sources. SIGKDD. ACM, 49--58.

Digital Library

[3]

Osei Appiah. 2001. Ethnic identification on adolescents' evaluations of advertisements. Journal of Advertising Research Vol. 41, 5 (2001), 7--22.

[4]

Elizabeth Aries and Kimberly Moorehead. 1989. The importance of ethnicity in the development of identity of Black adolescents. Psychological Reports Vol. 65, 1 (1989), 75--82.

[5]

Yambazi Banda, Mark N Kvale, Thomas J Hoffmann, Stephanie E Hesselson, Dilrini Ranatunga, Hua Tang, Chiara Sabatti, Lisa A Croen, Brad P Dispensa, Mary Henderson, et almbox. 2015. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics, Vol. 200, 4 (2015), 1285--1295.

[6]

Donald A Barr. 2014. Health disparities in the United States: Social class, race, ethnicity, and health. JHU Press.

[7]

Yoshua Bengio and Greg Corrado. 2015. Bilbowa: Fast bilingual distributed representations without word alignments. ICML (2015).

Digital Library

[8]

Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research Vol. 3, Feb (2003), 1137--1155.

Digital Library

[9]

Robert W Buechley. 1976. Generally useful ethnic search system: GUESS. In Annual Meeting of the American Names Society.

[10]

Esteban González Burchard, Elad Ziv, Eliseo J Pérez-Stable, and Dean Sheppard. 2003. The importance of race and ethnic background in biomedical research and clinical practice. The New England journal of medicine Vol. 348, 12 (2003), 1170.

[11]

Jonathan Chang, Itamar Rosenn, Lars Backstrom, and Cameron Marlow. 2010. ePluribus: Ethnicity on Social Networks. ICWSM Vol. 10, 18--25.

[12]

Andrew J Coldman, Terry Braun, and Richard P Gallagher. 1988. The classification of ethnic status using name information. Journal of epidemiology and community health, Vol. 42, 4 (1988), 390--395.

[13]

Seeromanie Harding, Howard Dews, and Stephen Ludi Simpson. 1999. The potential to identify South Asians using a computerised algorithm to classify names. Population Trends London (1999), 46--49.

[14]

Yifan Hu, Emden Gansner, and Stephen Kobourov. 2010. Visualizing graphs and clusters as maps. IEEE Computer Graphics and Applications Vol. 30 (2010), 54--66.

Digital Library

[15]

Gueorgi Kossinets and Duncan J Watts. 2009. Origins of homophily in an evolving social network 1. American journal of sociology Vol. 115, 2 (2009), 405--450.

[16]

Diane S Lauderdale and Bert Kestenbaum. 2000. Asian American ethnic identification by surname. Population Research and Policy Review Vol. 19, 3 (2000), 283--300.

[17]

Quoc V Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. ICML, Vol. Vol. 14.

Digital Library

[18]

Jure Leskovec and Eric Horvitz. 2008. Planetary-scale views on a large instant-messaging network WWW. ACM, 915--924.

Digital Library

[19]

Pablo Mateos. 2007. A review of name-based ethnicity classification methods and their potential in population studies. Population, Space and Place Vol. 13, 4 (2007), 243--263.

[20]

Pablo Mateos, Richard Webber, and PA Longley. 2007. The cultural, ethnic and linguistic classification of populations and neighbourhoods using personal names. (2007).

[21]

Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology Vol. 27, 1 (2001), 415--444.

[22]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality NIPS. 3111--3119.

Digital Library

[23]

Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global Vectors for Word Representation. In EMNLP, Vol. Vol. 14. 1532--43.

[24]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations SIGKDD. ACM, 701--710.

Digital Library

[25]

James Quesada, Laurie Kain Hart, and Philippe Bourgois. 2011. Structural vulnerability and health: Latino migrant laborers in the United States. Medical Anthropology, Vol. 30, 4 (2011), 339--362.

[26]

Maja Rudolph, Francisco Ruiz, Stephan Mandt, and David Blei. 2016. Exponential Family Embeddings. In NIPS. 478--486.

Digital Library

[27]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067--1077.

Digital Library

[28]

Vetle I Torvik and Sneha Agarwal. 2016. Ethnea--an instance-based ethnicity classifier based on geo-coded author names in a large-scale bibliographic database. International Symposium on Science of Science (2016).

[29]

Pucktada Treeratpituk and C Lee Giles. 2012. Name-ethnicity classification and ethnicity-sensitive name matching. AAAI.

Digital Library

[30]

Laurens Van Der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. Journal of Machine Learning Research Vol. 15, 1 (2014), 3221--3245.

Digital Library

[31]

Zhaohui Wu, Dayu Yuan, Pucktada Treeratpituk, and C Lee Giles. 2014. Science and Ethnicity: How Ethnicities Shape the Evolution of Computer Science Research Community. arXiv preprint arXiv:1411.1129 (2014).

Cited By

Davidson NGreene C(2024)Analysis of science journalism reveals gender and regional disparities in coverageeLife10.7554/eLife.84855.312Online publication date: 28-May-2024
https://doi.org/10.7554/eLife.84855.3
Davidson NGreene C(2024)Analysis of science journalism reveals gender and regional disparities in coverageeLife10.7554/eLife.8485512Online publication date: 28-May-2024
https://doi.org/10.7554/eLife.84855
Gök AMacmillan GChen BKaraulova M(2024)Measuring Cultural and Ethnic Diversity in Research and InnovationSSRN Electronic Journal10.2139/ssrn.4854150Online publication date: 2024
https://doi.org/10.2139/ssrn.4854150
Show More Cited By

Index Terms

Nationality Classification Using Name Embeddings
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. Social and professional topics
  1. User characteristics
    1. Race and ethnicity

Recommendations

Name-Nationality Classification Technology under Keras Deep Learning
BDE '20: Proceedings of the 2020 2nd International Conference on Big Data Engineering

To improve the classification efficiency of personnel nationality information, the classification method of personnel nationality information is explored. First, a new classification method is proposed using the Keras model and deep learning theory. Two ...
Name-ethnicity classification from open sources
KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

The problem of ethnicity identification from names has a variety of important applications, including biomedical research, demographic studies, and marketing. Here we report on the development of an ethnicity classifier where all training data is ...
Multimodal 2D and 3D Facial Ethnicity Classification
ICIG '09: Proceedings of the 2009 Fifth International Conference on Image and Graphics

Ethnicity is an important demographic attribute of human beings, and automatic face-based classification of ethnicity has promising applications in various fields. In this paper, we explore the ethnicity discriminability of both 2D and 3D face features, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSF

Conference

CIKM '17

Sponsor:

CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

Acceptance Rates

CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

53
Total Citations
View Citations
6,150
Total Downloads

Downloads (Last 12 months)2,412
Downloads (Last 6 weeks)195

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Davidson NGreene C(2024)Analysis of science journalism reveals gender and regional disparities in coverageeLife10.7554/eLife.84855.312Online publication date: 28-May-2024
https://doi.org/10.7554/eLife.84855.3
Davidson NGreene C(2024)Analysis of science journalism reveals gender and regional disparities in coverageeLife10.7554/eLife.8485512Online publication date: 28-May-2024
https://doi.org/10.7554/eLife.84855
Gök AMacmillan GChen BKaraulova M(2024)Measuring Cultural and Ethnic Diversity in Research and InnovationSSRN Electronic Journal10.2139/ssrn.4854150Online publication date: 2024
https://doi.org/10.2139/ssrn.4854150
Canayaz MDarendeli A(2024)Country Reputation and Corporate ActivityManagement Science10.1287/mnsc.2023.475370:3(1483-1504)Online publication date: Mar-2024
https://doi.org/10.1287/mnsc.2023.4753
Ferreira FKenney JSmith B(2024)Household Mobility, Networks, and Gentrification of Minority Neighborhoods in the United StatesJournal of Labor Economics10.1086/72880542:S1(S61-S94)Online publication date: 1-Apr-2024
https://doi.org/10.1086/728805
Fry CTran S(2024)The price of mistrust: the impact of a medical ethics scandal on scientific capacity in Sub-Saharan AfricaIndustry and Innovation10.1080/13662716.2024.2421933(1-24)Online publication date: 2-Nov-2024
https://doi.org/10.1080/13662716.2024.2421933
AlShebli BMemon SEvans JRahwan T(2024)China and the U.S. produce more impactful AI research when collaborating togetherScientific Reports10.1038/s41598-024-79863-514:1Online publication date: 19-Nov-2024
https://doi.org/10.1038/s41598-024-79863-5
Ganji GKale AKale D(2024)“Is cash better with color?” – Do CEOs of color generate higher value of cash?Economics Letters10.1016/j.econlet.2024.112007244(112007)Online publication date: Nov-2024
https://doi.org/10.1016/j.econlet.2024.112007
An RTian FZhang Y(2024)Individualistic CEOs and financial misstatementsReview of Quantitative Finance and Accounting10.1007/s11156-024-01364-3Online publication date: 20-Nov-2024
https://doi.org/10.1007/s11156-024-01364-3
Picazo-Sanchez POrtiz-Martin L(2024)Analysing the impact of ChatGPT in researchApplied Intelligence10.1007/s10489-024-05298-054:5(4172-4188)Online publication date: 21-Mar-2024
https://doi.org/10.1007/s10489-024-05298-0
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents