Nationality Classification Using Name Embeddings
Junting Ye1, Shuchu Han4, Yifan Hu2, Baris Coskun3* , Meizhu Liu2, Hong Qin1, Steven Skiena1 1Stony Brook University, 2Yahoo! Research, 3Amazon AI, 4NEC Labs America {juyye,shhan,qin,skiena}@cs.stonybrook.edu,{yifanhu,meizhu}@oath.com,[email protected]
ABSTRACT Ethnicity Nationality (Lv1) Ritwik Kumar Ravi Kumar Ethnicity Nationality identication unlocks important demographic infor- Muthu Muthukrishnan Mohak Shah Black mation, with many applications in biomedical and sociological Deepak Agarwal White Ying Li research. Existing name-based nationality classiers use name sub- Lei Li API Jianyong Wang AIAN strings as features and are trained on small, unrepresentative sets Yan Liu Shipeng Yu 2PRACE of labeled names, typically extracted from Wikipedia. As a result, HangHang Tong Hispanic Aijun An these methods achieve limited performance and cannot support Qiaozhu Mei Jingrui He ne-grained classication. Xiaoguang Wang Tiger Zhang We exploit the phenomena of homophily in communication pat- Jing Gao Faisal Farooq Nationality (Lv1) terns to learn name embeddings, a new representation that encodes Rayid Ghani Usama Fayyad African gender, ethnicity, and nationality which is readily applicable to Leman Akoglu European Danai Koutra CelticEnglish building classiers and other systems. rough our analysis of 57M Evangelos Simoudis Evangelos Milios Greek Marko Grobelnik Jewish contact lists from a major Internet company, we are able to design a Tijl De Bie Claudia Perlich Muslim ne-grained nationality classier covering 39 groups representing Charles Elkan Nordic Diana Inkpen over 90% of the world population. In an evaluation against other Jennifer Neville EastAsian published systems over 13 common classes, our F1 score (0.795) is Derek Young SouthAsian Andrew Tomkins Hispanic Tina Eliassi-Rad substantial beer than our closest competitor Ethnea (0.580). To Stan Matwin Roberto J. Bayardo the best of our knowledge, this is the most accurate, ne-grained Romer Rosales nationality classier available. 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 As a social media application, we apply our classiers to the Probability Probability followers of major Twier celebrities over six dierent domains. We demonstrate stark dierences in the ethnicities of the followers of Figure 1: Ethnicity and nationality (Level 1 of the taxonomy) Trump and Obama, and in the sports and entertainments favored by classication on some data mining researchers. dierent groups. Finally, we identify an anomalous political gure whose presumably inated following appears largely incapable of reading the language he posts in. to reect genetic dierences [5, 10] and public health disparity [6, 25] among groups. Nationality identication is also important CCS CONCEPTS in ads targeting, academic studies of political campaigns and social •Social and professional topics → Race and ethnicity; •Com- media analysis [3, 11]. Name analysis is oen the only practical puting methodologies → Information extraction; way to gather ethnicity/nationality annotations, because of privacy concerns. KEYWORDS Several previous name-based ethnicity/nationality classication Nationality classication; ethnicity classication; name embedding; approaches have been presented [11, 28, 29], including [2] at KDD ’09. However, the performance of these methods has been con- strained by small and artical training sets, such as celebrity names 1 INTRODUCTION from Wikipedia, and restricted to coarse ethnicity/nationality tax- arXiv:1708.07903v1 [cs.SI] 25 Aug 2017 Nationality and ethnicity are important demographic categoriza- onomies. e long tail of names makes these approaches dependent tions of people, standing in as proxies to represent a range of cul- on surface forms (like substring distributions), which are by de- tural and historical experiences. Names are important markers of nition ineective for logograms. Almost all existing methods are cultural diversity, and have oen served as the basis of automatic designed only for Latinized names, while other writing systems nationality classication for biomedical and sociological research. (e.g. Arabic, Cyrillic) are also widely used. For example, nationality from names has been used as a proxy In this paper, we present NamePrism, a new name nationality and ethnicity classier which oers a ner-grained taxonomy of *is research was conducted when Baris Coskun was with Yahoo! Research. ethnic groups. Fig. 1 demonstrates the performance of our system, Permission to make digital or hard copies of part or all of this work for personal or by presenting the ethnicity/nationality probability distributions of classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation some data mining researchers. We believe our results will generally on the rst page. Copyrights for third-party components of this work must be honored. agree with the reader’s judgement. For all other uses, contact the owner/author(s). Unlike previous methods that rely on substring features, we Conference’17, Washington, DC, USA © 2016 Copyright held by the owner/author(s). 978-x-xxxx-xxxx-x/YY/MM...$15.00 propose a more robust representation of names, which exploits DOI: 10.1145/nnnnnnn.nnnnnnn the phenomenon of homophily in communication. e homophily principle, that people tend to associate with similar people or pop- and (3) the follower counts of a particular Indonesian politi- ularly that “birds of a feather ock together,” is one of the most cian has been articially inated by Russian names. striking and empirically robust regularities in social life [15, 21]. e rest of this paper is organized as following. In Sec. 2, we Leskovec and Horvitz observed that, in instant messages, people introduce related works. Sect. 3 shows visualization and evaluations tend to communicate more frequently with others of similar age, of name embeddings. In Sec. 4 and 5, we describe the methodology language and location [18]. We analyze over 57 million contact lists and experiments of NamePrism and NamePrisme . We apply our from an email company, where the account holders are anonymized. methods on Twier celebrities in Sec. 6. e homophily-induced coherence of these contact lists enables us to derive meaningful features using word embedding methods 2 RELATED WORK [22, 23] as the basis for a comprehensive and eective nationality classier. Name nationality classication is a fundamental problem with a va- We collected 74M labeled names come from 118 dierent coun- riety of important applications: (i) biomedical research and clinical tries, containing over 90% of world’s population. We use these practice: it is critical to study the genetic and dietary dierences labels to dene a natural taxonomy of 39 leaf nationalities. As far among distinct groups[5, 10]. (ii) sociology: health care/ employ- as we know, our classier is the most ne-grained and eective one ment/ education disparities among dierent people. [6, 25] (iii) accessible to the public. e main contributions of our work are: online targeting: recommend more accurate ads/news/social me- dia posts to users [3, 11]. Other applications includes population • Introducing Name Embeddings: e contact-list derived demographic studies [4, 16, 19, 20]. Despite wide-spread demand name embeddings prove to be a powerful way to capture for nationality labels, it is hard to collect such information via latent properties of gender, nationality, and age in features self-reporting because of privacy concerns. Meanwhile, manual readily applicable to classication and regression tasks. annotation of nationality by names is, in fact, a very dicult task, Projections of these embeddings are very compelling, cre- especially for ne-grained taxonomy. ating maps in embedding space that correspond to maps Most recent works use name substrings as features for ethnic- of national boundaries. We believe these embeddings will ity/nationality classication [2, 11, 28, 29]. Ambekar et. al. [2] prove widely applicable to other applications and domains, propose to combine decision tree and HMM to conduct classica- including those in data privacy and security. tion on a taxonomy with 13 leaf classes. Treeratpituk et. al. [29] • Improved Nationality Classication: Our name-based na- utilize both alphabet and phonetics sequences in names to improve tionality classier NamePrism performs considerably beer performance and applied it to analyze how ethnicities evolves in than previous classiers. In particular, on a 13-class evalu- computer science research community [31]. Chang et. al. [11] ation over email/Twier data, our F1 score (0.795) proves use Bayesian methods to infer ethnicity of Facebook users with to be much beer than competing systems Ethnea1 (0.580) U.S. census data and study the interactions between ethnic groups. [28], HMM2 (0.364) [2], and (on a reduced 10-class scale) Torvik and Agarwal [28] propose instance-based classiers by us- EthnicSeer3 (0.571) [29]. NamePrism uses a Naive Bayes ing scientists’ names from PubMed. In comparison, we propose approach within a nationality taxonomy over 39 leaf nodes, name embedding in the light of homophily principle in social life employing name embeddings as the primary features. [15, 18, 21]. It is a beer representation because substrings are • Improved Ethnicity Classication: A benet of ne-grained limited to phonogram. Other relevant eorts are binary ethnicity nationality taxonomy is its exibility to apply to dierent classiers, including Hispanic [9], Chinese [12], South Asian [13]. task seings.e six ethnic groups dened by U.S. Census Name embedding is inspired by word embedding[8, 22, 23], Bureau over U.S. population largely corresponds to distinct which has many applications in natural language processing [1, 7, nations of origin. Our ethnicity classier NamePrisme , sim- 17]. Other types of data can also benet from the same assumptions ply reduces the nationality taxonomy from 39 leaf nodes to that underlie word embeddings, namely that a data point is gov- 6 and incorporates census-based ground truth parameters erned by the other data in its context [24, 26, 27]. DeepWalk [24] into the Naive Bayes model. learns node embeddings for graph data. It generates contexts by • Online Classication Resources: We release NamePrism as simulating random walks on graphs. Rudolph et. al. [26] propose a free web service4 for research in sociology, linguistics, and more general formulation of learning embeddings in dierent appli- biomedical applications. To the best of our knowledge, it is cation seings. Similarly, name embeddings treats email contacts the only nationality classier that handles various writing with most recency and frequency as context. systems, and works on a ne-grained 39-class taxonomy. • Social Media Analysis: We use NamePrism to analyze social media, specically the followers’ nationalities/ethnicities 3 NAME EMBEDDINGS of 600 major celebrities on Twier. Our results show that: Name embedding is a variation of word embedding. In a nutshell, (1) Donald Trump’s U.S. followers are disproportionally word embedding algorithms [8, 22, 23] aim to learn similar embed- White with followers of Obama and Clinton, (2) ethnicities dings (vectors) if two words co-occur frequently in their contexts. exhibit dierent preferences in sports and entertainment, In articles, the context of a word are naturally the words around it. To generate context in contact lists, we need to assign orders to 1 hp://abel.lis.illinois.edu/cgi-bin/ethnea/search.py contacts. In the light of homophily principle, we weigh contacts 2hp://www.textmap.com/ethnicity/ 3hp://singularity.ist.psu.edu/ethnicity by recency and frequency of communications. As a result, names 4NamePrism open API: hp://www.name-prism.com/ with large weights tend to have same nationalities. In this way, we KAMRAN
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OSELYN LIONEL SOPHIE ROSANNE ADO ANDR BRENO REBECA DORA VALENTINA DAISY BERNARD J OELLE CAMILLE NADINE MARYLOU CRISTIANO LISANDRA SOFIA GLORIA DOLORES FABIANO WILLIAN OSMAR GERSON ADRIANA ALE ROSITA ELBA GILBERT HUBERT BRIGITTE ALESSANDRA ALESSANDRO ALEXANDRE RENATO SOCORRO VIOLETA J OHANNE ADEMAR CLEITON VIVI ELOISA MIRNA YESSENIA ARIANA LISE ESTELLE FLORENCE ROSEANN ALINE ARTUR CLUDIO FABIOLA ILEANA FLOR HAYDEE HIRAM GENESIS ODETTE SACHA J ULIETTE ALIX MARIBETH VAGNER REINALDO MARTHA ROXANNA RUDY CLAUDE J EFERSON DANILO FLAVIO MARCOS AUREA ANGELITA ANY RENE ARMAND CHRISTEL NAIARA MAURCIO MOISS ROXANA ELSA ILIANA GINETTE ALDA TIAGO J ULIANO NETO ANNY ELIEZER HILDA KARLA MICHELINE MURIEL MAURA CASSIO ELIAS J OSU CARMEN YESENIA CLAUDINE CECILE FABIO LUCIO EDITH NORMA ALEXIA SILVIO MAURO EDNA ANA ANGLICA CORINA REMY FRANCOIS J OSY J ULIANE MARCELLO PALOMA ISAIAS MISAEL MIRIAM J OCELYNE BRIGID ANE DAVI GUILHERME MURILO MATEUS AUGUSTO J OSEFA RAFA BRAULIO TERESITA BERTA CONSUELO MARISOL MAYRA LAURENCE EMILIE CEZAR DELIA DENISSE ESTELLA CHANTAL MARIELLE CELINE NANDA J EANE J UANA ANGELICA ELIE EMILE J OICE FABIANE LUCIANO FABRICIO LEONARDO HELLEN ANTONIA LETICIA GILBERTO MOISES ROSA ANAIS J ACQUES DIONE HENRIQUE RENAN HOMERO EDGARD LIZETTE MICHLE J EANMARIE TAIS THIAGO MAICON FRANCISCA RAFAEL LOURDES MARIBEL GENEVIVE RAQUEL EDMUNDO AMALIA YADIRA LETTY NATHALIE FRANCOISE GIOVANA GRAZIELA ESTRELLA ESPERANZA J AZMIN GEORGES LUCIE GISLAINE RODRIGO FELIPE LINO PIERRE FRANCIELE FILIPE LEANDRO PEDRO J OS HUMBERTO MARCO CLARICE MARIANE VINCIUS VINICIUS MARCELO CLAUDIO TANIA CELIA LVARO AURORA ROSALIA ELVIRA IVONNE BERTHA ELVA FRANCE MARTINE ESTER LAZARO AXEL ARLETTE LUC ISABELLE ANNICK BIA MARCELINO NOEMI MARGARITA SARAI VIVIANE DAIANE BRUNO LUCAS LILIAN ISA ANDRIA FREDERICO MATHEUS SONIA ISABEL BERNARDO RICARDO ANTONIO OSWALDO MARILU YESSICA LUPE SERGE MANON SIMONE POLIANA VITOR MIRIAN ANABEL LIZETH NATACHA FRANOISE MAUD ELISA GILDA MIREYA CHRISTIAN EVELYNE BETE CSAR SANTOS ELDA LIZBETH MINERVA ELIETE LUCIANE CIBELE DANIELE RAPHAEL FABIANA VILMA FAUSTO RAMON XAVIER FABRCIO ROSI CINTHIA MARI CELINA EDUARDO ISMAEL J EANPAUL VALRIE VERONIQUE CATIA MARIA ROSSANA SUSY ROSALINDA HENRI SYLVIE PASCALE CHRISTELLE CIDA DEISE ELIS ERICK J OSUE ROSALBA ELVIA MARISELA MARYSE IVONE ANDRESSA DIOGO LUCIANA ROBERTO CESAR DIMITRI CINTIA MAGDA FRANCISCO REN NARA KELLEN SUELLEN SAMANTA LIGIA ELIDA FERNANDO VICENTE AURELIO BLANCA IRMA CARINE CLAUDIA BEATRIZ EMANUEL ESMERALDA CRISTAL NENA MLANIE HLNE EMMANUELLE ELZA ELIANE PATRCIA GABRIEL MAURICIO RIGOBERTO ALONDRA YAZMIN J EANCLAUDE VRONIQUE CLEIDE MARLI MARGARETE FERNANDA MAIRA SERGIO ANGEL ARACELY MIREILLE J EANPIERRE IR NEIDE DAIANA J ULIO J ACKY TAMIRES RAFAELA MAIARA DEBORA ROSANA ELIANA EMILIA LIDIA CARLOS LEONEL IVAN FELIX HERIBERTO CAROLINA MARICELA DENIS NEUSA TATIANE CAMILA DIEGO GUSTAVO AMERICA SYLVAIN DELPHINE ANDREZA KARINA RODOLFO ALFONSO ALMA DULCE REYNA IMELDA YVES GRARD J ANAINA THAIS SILVANA TATIANA GISELLE LUCIA HUGO LEOPOLDO ALONSO J ANETE ROSANGELA THALITA SUELEN BRUNA KAROLINE CRISTINA MARIO VICTOR EFRAIN RPONDRE FLAVIA LORENA J ONATAN AMADO ISELA MICHEL GILLES CLIA ROSELI J OSE ARMANDO ELIA MATHIEU MARCELA LILIA RUBI ETIENNE REMI ELISABETE LUANA J SSICA NAYARA MILENA NATALI DANIELA GABRIELA ROQUE EUGENIO FLIX J ORGE CRUZ AZUCENA FABIENNE SUELI REGIANE NATHLIA GREGORIO BATRICE BRBARA MNICA VERNICA PAOLA ANAHI XOCHITL FRANOIS LAURE MARISTELA GLAUCIA THAS LUIS EDGAR PERLA BENOIT LARISSA MARINA ELIO ALVARO MAXIME CCILE NATHALIA MARIANA SILVIA GUILLERMINA BIBIANA OSVALDO ARNULFO REYES LUCERO SUELY LUDMILA MATILDE ITZEL NAYELI J EANLUC J EANMARC CLINE DAYANE PRISCILA MARA SUSANA SAMARA SELMA ANDREIA ROSANE NATALIA MANUEL J AIME OFELIA STPHANE VALRIA LETCIA TALITA IVANA ALBERTO ALFREDO MARCEL CHRISTIANE MARGARETH VALERIA MARTA MALENA LILIANA FIDEL ARTURO TEREZINHA ESTELA ROCIO FANNY STEPHANE IZABEL CRISTIANE ELISANGELA LAS ROLANDO SELENE MARISA MIGUEL ROSARIO ARACELI SEBASTIEN VANDA MEIRE CSSIA J OELMA MAYARA FLVIA CLAU CECILIA CECI ALEJ ANDRA GUADALUPE MYRIAM DANY INS INES CRISTOBAL BENITO SANDRINE APARECIDA MARIZA LUCILA J OSEFINA MERCEDES VIVIANA LETY PASCAL FREDERIC LUZIA MRCIA LAIS LUCRECIA GRACIELA ALEJ ANDRO OSCAR RAUL J ESUS NOE BERENICE YANNICK VALE FER NATY LEANDRA NATLIA LILI AGUSTIN DOMINGO ELISEO LUPITA LULU MAGALI PHILIPPE EFREN VIRGINIE VERA MARILENE FAMILIA FRDRIC LUCIENE KTIA EVANGELINA MIRTA ESTEFANIA ALEJ O ERNESTO HECTOR ELOY TERE SNIA DBORA RAFAELLA MONI ANDRES GENARO OLIVIER STPHANIE J ULIANA EMA MABEL ADRIN EMILIO ISIDRO ROSY ALAIN J ESSY GUILLAUME SALVADOR HERVE ELODIE CLUDIA RENATA CAROLA GISELA AURLIE GISELE MARIEL MARIANELA RUBEN RAYMUNDO MARICARMEN BERTRAND CARMEM LIDIANE AGUSTN ANDRS BETY PATY THIERRY SUZANA CASSIA ISADORA CARINA MARTINEZ RAMIRO J AVIER GERARDO ROGELIO ADRIEN DIDIER J ULIEN REJ ANE MATHILDE MARITA MARIELA IRVING FRANCK J AQUELINE J OSIANE REGIS LUNA LOPEZ SANCHEZ ARIADNA HELOISA SORAIA J ANANA ARIANE CATALINA J UAN ENRIQUE LALO SAUL AIME ADELINE LAURENT GARCIA ANIBAL OMAR OCTAVIO ULISES MARILIA IARA ANGELES LAETITIA VNIA LILIANE GOMEZ XIMENA FABRICE ROSNGELA J LIA SOL YESICA SOFA MARU ZULEMA SBASTIEN LCIA PEREZ RODRIGUEZ J OAQUIN ISRAEL ANGE HERV CHRISTOPHE J IMENA EDGARDO GABY FTIMA TEREZA KAMILA LVIA RAISSA J ULIETA CRISTIAN AURELIE KARINE MACARENA CONSTANZA HERNANDEZ MME VANIA DIAZ LORE URIEL GRISELDA LUDOVIC SOLEDAD MATEO J ACOBO VERO YANN FABIEN GRAA CATARINA ROSILENE EVELINE TOMAS PEPE MATTHIEU AMANDINE NGELA CAMILLA MARCELLE GONZALEZ EUGENIA PATRICIO TORRES HCTOR RAMN PAULINA NORA MAMAN RUI IZABELA PAZ MONTSERRAT GEMA ROMAIN MARLIA RIKA GUILLERMO MARINE AGNS MARGARIDA ROMERO BELEN MILENE ANABELLA YANINA AMLIE LIANE GERMAN IVN ROCO J ESS PACO LE HELENA TNIA RICA J USSARA MELINA ARNAUD CABINET FLORES AGENCE TATI SAN AMELIE LUIZA NOELIA VANESA FABIAN NESTOR VCTOR MANOLO J OANA ISABELA RAL PILAR NURIA YARA MARLY MELISA ANDRA FLORENCIA ESTEBAN SANTIAGO IGNACIO RUBN J OSI SOLANGE ARQ MICA MICAELA GONZALO J ORDI CRISTIANA KATIA ING ROMINA SILVINA LIVIA YAMILA MAITE LIC J ESICA HERNAN J ULIN MAR LUCI TELMA CP ANALIA CRIS PABLO DARIO LEDA VANINA FABIN DANI HERNN GERMN SEBASTIN NACHO NICOLS ARIEL DANTE HORACIO NORBERTO MAXIMILIANO MARTN TOMS MARIANO MATAS MATIAS FEDERICO EZEQUIEL FRANCO CHARLY AGUSTINA ESTUDIO EMILIANO J ULIAN FACUNDO CABAAS CARO SEBASTIAN MARTIN GASTON DAMIAN AGUS GUIDO MAXI NICOLAS NICO
Figure 2: 2D projection (le ) of 5K popular rst names’ embeddings. Orange are male names, salmon for females and gray for unlabeled. Same-gender names cluster together, indicating similar embeddings. Inset of the male-female border (right) shows more neutral names.
HAM
AKERS MORROW CARNES
CHILDRESS
WORLEY THACKER
SHEPARD
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VANCE HAWK LEDFORD THURMAN POSEY HESTER GARRISON CANTRELL SHAVER TABOR BOGGS SELF HANKS THOMASON ELKINS PROCTOR PRITCHETT FARMER UNDERWOOD LOTT JUSTICE MADDOX YARBROUGH HAYS BEAVER CRAFT COCHRAN MOODY MERRITT BRANNON MCCARTY GILMORE LEDBETTER WINN RUFF STRINGER EASON CRUM CREWS MOTT WESTBROOK SAMS DODSON DOTSON CLINE KISER COUCH BLEVINS LANDERS SAPP SHIELDS KINSEY CARVER VARNER COVINGTON MALLORY SHIPMAN PRATER LANCASTER BARBOUR MCKINNEY BRITT ALFORD HELM HAMM FAIRCHILD DURHAM PIERCE BREWER REEDER RIGGS REAGAN MELTON GARNER BEAM HURST HATFIELD STINSON COBB WILEY BAGLEY WELDON BLACKBURN HERNDON KIRKLAND MOCK LILLY DAILEY SHEFFIELD FERRELL WINTERS BUNCH THORNTON RAINES WHITLOCK BELCHER SELLERS HARRELL VAUGHN CALHOUN NEWBERRY BUTCHER HAMLIN BYRD YANCEY PARRISH CALLAWAY COOLEY DUNHAM DICKEY STOVER HICKMAN ROWAN SHIPLEY NAPIER RAMSEY WISE NICKERSON BUTTS GRIMES WALLS PHELPS BRIDGES GOODSON MCCAIN BURRIS HILTON GODWIN EWING MCGEE COOMBS MCCULLOUGH COE MILLIGAN PENDLETON CROUCH DOWNS HARMON HUFFMAN KENDRICK HALEY UPTON GARLAND HOSKINS WOOTEN PACE HANNAH BOND MCCALL GREER SHOOK KELLEY ALLEY HUSTON EDMONDSON KIMBLE ENGLISH MAGEE MARKHAM CLEMENTS LEONARD TRENT CROWELL SWANN MALONE BRADFORD GUNN VINSON EMERSON MILNER LESTER PATTON WHITAKER FOUNTAIN MCCORD LONG HAWKINS CORLEY SHOEMAKER MCCRACKEN GOFF DRAKE GRAVES CUMMINS TRIMBLE STAFFORD STAPLES RICHMOND MCCOLLUM HUDDLESTON DENT HANEY ELDRIDGE CARSON NICHOLS NEWTON HORTON RICHEY GORE TRIPP CARLTON WITHERS CLEVELAND BURNETTE NUGENT BOWER PRESTON DICKENS CRANDALL STAPLETON BACON ARNOLD CHILDS MCFARLAND WILCOX LOWERY ODELL SAWYER LANE BEARD REED NEELY CODY JARVIS GAGE PRIEST BROWNING CROCKETT BRAGG CROCKER HOBBS BLEDSOE HODGES DOBBS MYERS MCCLURE GREGG FULTON BLACKMON GAY LADD LYNN WALDEN SUMMERS NANCE STORM CROWE JEWELL SANFORD LOCKE PARKS LACY HOGUE SPIVEY BURROUGHS IRELAND BYERS ATWOOD LINN TOMPKINS BRYSON MCBRIDE CANNON BALLARD STILES STONE BURCH PAYNE CALDWELL WOMACK SCRUGGS CONLEY JAMESON LYLE AVERY HERRON HANKINS HIGHTOWER FAGAN COPE LYNNE HORN CORNELIUS POOLE WILKERSON CHAMPION WHITMAN BRAY DOVE AKINS SEAY LEMON PAINTER HYATT KIDD BERRY MACKEY JOINER RUSH CRUMP PYLE ENGLAND WELCH BUCHANAN HODGE ONEAL FORT MORLEY HINKLE BARNHART CORNWELL CULVER BUCK RANKIN BOWMAN MASSEY REEVES GANT RICE WYATT YATES FENNELL TALLEY ARRINGTON SPAIN HIGGINS HOLT SKINNER MERCER KEENE EVERETT MEDLEY HUTCHINS CARPENTER WHEELER RAY DOWNING WATKINS COLLIER SMYTH PARR METCALF WEEKS SUTTON MCDOWELL HEDRICK PURVIS KIRKPATRICK SHARP SNIDER LATHAM SCARBOROUGH JERNIGAN WASHBURN COX LITTLE MORTON TATUM GILLESPIE SNOW STANLEY LANGFORD WILKES GALLOWAY JEFFRIES SLACK PICKETT ROLLINS ECHOLS BLACKWELL WALLER POPE ANDERS ELDER KENYON BENTLEY MONK KIRK LAMB MILLER BOWDEN GOODWIN BOWLES COOK DRAPER HUMPHREY WHITLEY DUFF KEMP GLASS DEAN BRANTLEY CARNEY PECK KNOTT WYLIE HOLLAND BAKER DAVENPORT SWAIN PRYOR SANDERS HERRING CULP EATON MICHAELS BATEMAN CRAVEN HUTTON PRICE TANNER RUDD SPEARS HOLLIDAY BLAND NORRIS FROST CLARK FOWLER ROBERSON KEY JENNINGS WEAVER HOWELL CAVANAUGH CALVERT STOREY HOLMAN PORTER CHESTER HORNE MCNEILL FINCH BULLARD ODOM DIETZ CONN CROSBY GROGAN HOLDEN POTTS HASKINS MCCOY OWENS PITTMAN WOODARD MCWILLIAMS WOOD BEAL CURTIS WELLS TONEY GIVENS BOLAND BATES BRADLEY ROGERS MOSS BUSH WARD MAYBERRY BASS WHALEN PHIPPS COATES POLLOCK GARVIN LAWSON WALTON HOPKINS ROUSE AUSTIN TUCKER CAREY KENT HITCHCOCK FISHER BARRETT CECIL LAYTON HEATH WADDELL MEANS GARRETT CORCORAN CLEARY HURLEY TILLEY GARDNER RAINEY STRICKLAND MOBLEY WILDER MANNING COFFEY STANFORD KNIGHT WARREN CHANDLER BARNETT PARKER BURTON HAYES DALTON BALDWIN MEREDITH HOOD HUNTER TERRY RODGERS BEASLEY CARLIN BILLINGS MATHEWS WILES EARLY THORNE BOSWELL WIGGINS BOWEN NOONAN MCNALLY BURNS BASSETT FARRAR GUTHRIE PHILLIPS GOINS FIELD CARROLL NORTON STARKEY ARMSTRONG HUGHES TODD GILBERT HEARN SHAW NIXON CRAWFORD MATTHEWS FREEMAN MELVIN MULLEN ROWELL MCCONNELL ALDRIDGE LOVELL WILKINS DUNLAP BURNHAM HAND REDDING PAGE FRENCH DAY WATTS GANNON BYRNE HICKEY COURTNEY HUMPHRIES COLLINS FOX PICKERING MEEK EVANS HARRISON SHANAHAN MORRISSEY MCGUIRE KINNEY NYE PIPER STILL WEBB DODD HUNT WEST MCKINLEY KING MOORE JORDAN SNEED REAVES STROUD NEWMAN ELLIOTT SMITH MCKNIGHT MASTERSON ROOT VICKERS DICKSON WATERS NEWSOME LEARY HANLON SWIFT GRIFFITH BOLTON ANDERSON PURCELL HASTINGS HAGER STEPHENS WHITE DELANEY YEAGER SPAULDING COBURN BARLOW SHEPHERD LAMBERT RUSSELL ROBERTS MAXWELL TAYLOR HUDSON PERKINS GLENN HOLBROOK LINCOLN KNOWLES SCOTT HOWARD TATE SAYLOR WINCHESTER BRADSHAW BISHOP ORR PITTS ELLISON KEARNS ROWLAND FOOTE NELSON GIPSON HEALEY DEVLIN MOONEY MCCORMICK WELLER HEATON CRAMER MCGINNIS DOUGLASS ADAMS MITCHELL GAVIN EASTMAN QUICK STUART HADLEY CARR MORRIS RHOADES SLATER GROVES PETERS HALL ALLEN GRIFFIN SIMS CROWDER CULLEN HORAN MCCANN ROBISON YODER LEA CARMICHAEL MAYNARD DYER BRITTON ROACH COLE YOUNG MCCABE PEPPER GRAY PATTERSON DOOLEY ENNIS FARR MURDOCK CHASE MCMILLAN HARVEY MORGAN TURNER RHODES FALLON KENNY CAHILL ABBOTT DUNN SHEPPARD THOMPSON WILSON CASEY JUDD WOLFE SNELL NASH RADFORD MARTIN COPELAND HARRINGTON BURK WORKMAN BARBER MARSH SADLER WATSON HENDERSON FS PIERSON SHORT WRAY REECE BARROW LOVETT ONEILL PLATT SWAN WHITMORE LEACH BRIGGS DUNCAN BENNETT MASON BLACK ROSS WALKER BELL KEYS MAGUIRE BARRY GLYNN KENNEY GIFFORD CUTLER STEVENS PRESCOTT WEBSTER DAVIS TOBIN NOLAN ADAMSON COWAN OSBORNE BARTON REDMAN BINGHAM HIGGS WRIGHT WHALEY SARGENT GATES LEIGH HART KIRBY CAIN ATKINS MILLS WOODS NEAL WHELAN GALVIN WINKLER ELLIS LOGAN HARPER CRONIN DILLON STRATTON DAUGHERTY MCGRAW BOOTH HENDRICKS BROWN HENLEY DUGGAN HANLEY CORBETT MCGOWAN ELLSWORTH HAINES POTTER PURDY FULLER HAMMOND WALLACE JOHNSON JETER KEATING DONOVAN OAKLEY MCKEE HOOPER WILLS BURR CHAPPELL DALY CHURCHILL STALEY PIKE DODGE HULL BURGESS CROSS CRAIG WESTON INGRAM DUFFY MERRILL NEFF BORDEN PEARSON ASHBY FLEMING FOSTER POWELL COOPER HILL PERRY GILLIAM MONAHAN MCCARTNEY BEEBE OLDHAM MASTERS PARSONS ANDREWS WHITEHEAD BROOKS MOSLEY OMALLEY MADDEN LINDER HOWE EASTON KEEN HOGAN SHAFER BLISS CRANE WILLARD JOHNSTON CASTLE REYNOLDS FLETCHER BAILEY CARTER COONEY CLANCY RHOADS BRUNER VAUGHAN RILEY HINTON CURRAN LAUGHLIN SPEAR HOBSON WHARTON VERNON GREGORY DENNIS DIXON PHELAN FITZPATRICK MCALLISTER CORBIN GREEN MAYFIELD BURKS ROONEY EGAN COYLE RUST PUTNAM IRWIN BECKETT PEACOCK ATKINSON LEWIS DONOHUE FLYNN HOOVER ROE SHAFFER FERRIS WALL ROBERTSON MCDONALD RUSS DAWSON JONES MCGHEE CLIFFORD SAGE KELLER ENGLE LOCKWOOD ROPER KENNEDY CHAPMAN LAWS WILLIAMS BRANCH DURBIN BEATTY HARTLEY WILKINSON SIMPSON SLADE MCAFEE GIBSON GOODE SPENCER GOLDEN QUINN WILLOUGHBY FERGUSON COATS MONROE CROWLEY KELLOGG RADER MANN GILL LAWTON STEELE SANDS JAMES RICHARDSON MCCARTHY ALBRIGHT LUTHER WHITING HUTCHISON TRAVIS JEFFERS MARSHALL OTOOLE CONNOLLY WALSH WINSLOW GILLETTE BROWER MANLEY WILLIAMSON DREW THOMAS SINGLETON FLOOD SHERMAN JACOBS GOLDSMITH PENN CLAYTON FLOYD RUCKER REILLY LYNCH HAWLEY POWERS WAUGH HUMPHREYS DURANT HAWTHORNE DONALDSON STEWART MEEKS DOYLE REGAN QUIGLEY DORAN KNAPP STARK SEYMOUR ALLISON THORPE ROBINSON FOLEY SPANGLER TUTTLE LANGLEY WEBBER DAVIDSON BRIGHT VALENTINE GRAHAM HUGGINS OCONNELL LENTZ SHEARER CHAMBERLAIN ALEXANDER OSHEA QUINLAN SPRAGUE SQUIRES BURNETT SEWELL SHARPE DUNBAR BOONE MCGILL DUKE MCFADDEN KEITH CRENSHAW BANKS OREILLY RYAN KELLY KLINE 1 BARKER MAJOR BALL FREDERICK EDWARDS HINES DILLARD MAY GIBBS OBRIEN ELY CONKLIN OWEN DAVISON CLINTON LOWE HAMILTON BURRELL DUDLEY BIGGS LLOYD TIERNEY DWYER SNYDER HARTMAN WORTHINGTON SALTER PRINCE HOLMES OSULLIVAN LARKIN REINHARDT DELONG MOSHER BARTLETT SANDERSON MCCAULEY DARBY PARROTT JACKSON MONTGOMERY ASKEW MATHIS ODONNELL CALLAHAN WALLIS SPRINGER CARRINGTON HARRIS BUTLER HOUSTON POLK MCGRATH CASSIDY SHULTZ PRATT BIRD SHANNON SAVAGE RANDALL NORMAN GLOVER JUDGE OCONNOR COON HATHAWAY BAIRD WALTERS LAYNE CARTWRIGHT SLOAN DOTY FLINT MURRAY LYONS CUNNINGHAM POLLARD SIMMONS BRENNAN STACK SULLIVAN MCMANUS MC ALDRICH JACOBSEN AMP TEMPLETON GREENWOOD OAKES GRANGER FARLEY HAYWOOD HOLLOWAY LANGSTON DUPREE COSTELLO GEIGER BARR BULL HAYNES BARNES WILLIS REESE MCDONOUGH OSBORN WISEMAN HAYDEN BLAIR MARCH ADDISON FRANKLIN GAFFNEY STODDARD STONER HORNER DORMAN FORSYTHE FORRESTER ARCHER BAIN RIDLEY KANE FEENEY WILLEY MANSFIELD GILCHRIST BREWSTER FOY TITUS DYSON DUGAN MAHER FAHEY MURPHY BUCKLEY NEVILLE SCHAEFFER CROOK KYLE HOOK MORRISON SAMPSON REDMOND SYKES WILBURN TIMMONS DOHERTY MALONEY DOWLING JOHANSEN SKELTON BURT BRISCOE STEVENSON STUBBS MYLES WARE ERVIN ZIMMERMAN FORSYTH MCMULLEN BROWNE JOLLY BELLAMY GRIER FORD MULLIGAN GOODRICH ANDERSEN MADSEN KINCAID PARISH TOMLINSON BRYAN LAWRENCE BOYCE COOKE MCRAE BULLOCK BOYD MCNAMARA GARVEY KEENAN BENSON SPRING NICHOLSON BARTLEY NESBITT BROUGHTON CONNELLY MCHUGH DOWNEY DAHL LOWRY BRUCE BLACKMAN SPEED GILES MAYES LENNON KELLEHER GALLAGHER CONNOR DEMPSEY KEMPER WITT WOODWARD GALE IRVING AMOS BOYKIN PEOPLES PAPPAS KAUFFMAN PEDERSEN WILDE NEWELL ISAACS HOLIDAY COLES KNOX TYLER BRODERICK GRAFF PETERSEN DALE DOBSON MCINTOSH PITT STEEL FINNEY PUGH EDMONDS 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MESQUITA RAMON CANDIDO BARROS LUCENA TOLEDO BRIONES FELIPE MORAES RABELO HUMBERTO LUIZ JUNIOR NERY ALFREDO RODRIGO MARCIO FERNANDES PAIVA RODRIGUES MARTINS FERREIRA MELLO CERQUEIRA RAMOS ERNESTO RENATO VAZ PEREIRA GUIMARAES FERNANDO SERGIO FIGUEIREDO LINS CAMPOS PEDRO TADEU AMARAL PAULO TAVARES GONALVES RIBEIRO LOBATO ALBERTO ANTNIO COUTINHO DIAS NUNES BISPO ADOLFO MARCOS SIMOES DELACRUZ LEONARDO VIEIRA COELHO MORAIS VASCONCELOS FERRER GUSTAVO AUGUSTO GERALDO PANTOJA MARQUES PACHECO LIRA MIGUEL FABIANO VITOR JNIOR HENRIQUES GOMES LUCIO TOLENTINO SORIANO PASCUAL OLX EMILIO SOARES RANGEL MATEO JULIO EDUARDO AGUIAR MENDONA BEZERRA RICARDO MARCELO LOPES MENDES GUIMARES ABAD LUIS FILHO OLIVEIRA SOUZA TEIXEIRA GUILHERME HENRIQUE TRINDADE BRAZ FRANCO SALVADOR FRIAS DOMINGO BENTO FAGUNDES LEITE PINHEIRO GUERRA CARLOS ROBERTO MONTEIRO TOMAS RAFAEL JORGE ALVES MAGALHES HUGO ANTONIO CLAUDIO EDSON JOO PINTO PIRES ALMEIDA BRAGA ALCANTARA TOSCANO AFONSO BATISTA CARVALHO POLO JOSE MAURO CAVALCANTE PATRICIO GREGORIO FRANCISCO VICENTE MAGNO BORGES CARNEIRO COSTA LEAL AURELIO LOBO FREITAS JOS NOGUEIRA LUCIANO FEITOSA PONTES CLEMENTE EDGAR EUGENIO JESUS ELIAS GONZAGA MACHADO SILVA NASCIMENTO ADRIANO CAMARGO NERI BARBOZA BARBOSA BASTOS MARINHO BRUNO SABINO ANDRADE AQUINO GODOY ANGELO LEO AZEVEDO DE FONSECA SANTOS LIMA DONATO LEMOS CONCEIO AMORIM FELIX MARIANO PAULINO SOUSA ESTEVES ROCHA MOTA CESAR DUARTE ARAUJO MELO CSAR CALDAS MAIA EVANGELISTA CEZAR GASPAR TEODORO MACIEL ABREU BRITO FREIRE MAGALHAES BERNARDO MATIAS SANTANA FARIAS CAVALCANTI PRADO SILVESTRE BARRETO ALENCAR CHAVES VIANA CORREA MATOS PORTELA MATTOS BUENO BANDEIRA QUEIROZ BARROSO VELOSO PIMENTEL MUNIZ GALVO COUTO PESSOA FRAGA SENA MOTTA FONTES FALCO BOTELHO
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Figure 3: 2D projection (le ) of 5K popular last names’ embeddings. Same-ethnicity names stand close indicating similar embeddings. Insets (le to right) highlight White 1 , Black 2 and Hispanic 3 names. API ( 4 and 5 ) in Fig. 4. construct a “sentence” by keeping top contacts of a sorted list. Note names with unknown gender gray. In general, names of the same that the ordering of sentences in an article is informative for word gender form mostly contiguous regions. Fig. 2 (right) is an inset embeddings. In contrast, the ordering of the contact lists is not showing a region along the male/female border. We can see that useful because email account holders are mutually independent. “Ollie” is labeled as a female name based on Census data (2:1 ratio of female/male instances), while in fact it is oen used as a nickname for “Oliver” or “Olivia” for daily use. erefore name embedding is 3.1 Name Embedding Visualization correct in placing it near the border. e embedding also correctly We use t-SNE [30] to project the 100D name embeddings into 2D, placed “Imani” and “Darian”, two names not labelled by the Census and create the map visualization with gvmap [14]. U.S. census data, near the border, but in the female/male regions, respectively. data are used as ground truths to visualize and evaluate name Fig. 3 (le) shows a map of last names. We color a name accord- embeddings. More specically, we use U.S. 1990 Census data to ing to the dominant ethnicity classication from 2000 Census data. label popular rst names (4.7K female and 1.2K male) and U.S. 2000 Four major ethnicities are White (pink), Black (orange), Hispanic Census data to label popular last names (115K White, 5K Black, 6K (yellow), and API (green). Names beyond census data are colored Asian/Pacic Islander (API), 0.2K American Indian/Alaskan Native gray. e three insets in Fig. 3 highlight the homogeneity of regions (AIAN), 0.1K Two or more races (2PRACE) and 7K Hispanics). As by ethnicities. White, Hispanic and API stand in large contiguous shown from Fig. 2 to Fig. 4, genders and ethnicities are labeled regions while Black are more dispersed. It makes sense because with dierent colors. We are surprised to see how names with same many Black people adopt White names during American slavery gender, ethnicity and nationality cluster together (Fig. 2 to Fig. 4). time. More interestingly, there are two distinct Asian regions in the Fig. 2 (le) illustrates the landscape of rst names. Using 1990 map. Fig. 4 presents insets for these two regions, revealing that one Census data, we color male names orange, female names pink, and Joint Seperate Metrics CBOW SG CBOW SG P (G |G ) 0.909 0.884 0.916 0.884 Gender 1 i i 5 P10(Gi |Gi ) 0.936 0.927 0.935 0.921 P1(W |W ) 0.936 0.946 0.930 0.922 P (B |B) 0.594 0.456 0.444 0.345 Ethnicity 1 4 P1(A|A) 0.763 0.721 0.717 0.680 P1(H |H ) 0.754 0.754 0.671 0.697 Table 1: Evaluations of dierent name embedding variants. CBOW and SG are two word embedding methods. P1(Gi |Gi ) is the probability that 1 nearest neighbor (1-NN) is of the same gender while P10(Gi |Gi ) is for 10-NN. “W”, “B”, “A”, “H” stand for “White”, “Black”, “API”, “Hispanic”, respectively.
last name. It is easy to predict one’s nationality if his last name is Figure 4: Two distinct Asian clusters. Le : Chinese/ Viet- unique to that nation. However, there are many last names that are popular across nationalities. For example, “Lee” is popular in both namese names ( 4 ). Right: Indian names ( 5 ). It shows China (especially in Hong Kong) and the UK. For “Qiang Lee” and name embeddings capture nationality signals. “John Lee”, we would make mistakes if we only take signals from the last name. Combining with rst names, we can perform beer because it is easy to see whether the rst name is more in China or cluster consists of Chinese and Vietnamese names (le) while the UK. Similarly, using both name parts also helps when names are other (right) contains Indian names. Even on the le subgure, Viet- mixtures due to immigration or cross-nationality marriage. namese names are more gathering around the boom part while Our method, NamePrism, can be formalized in Eq. 1: Chinese names on the top. ese observations strongly indicate name embeddings capture gender, ethnicity and nationality signals. P(N |vf ,vl ) ∝ P(vf |N )P(vl |N )P(N ) (1) 3.2 Evaluation where N denotes nationality, vl means last name and vf is rst We run experiments to validate our observations quantitatively name. We will describe our methods to estimate the likelihood (i.e. P(v |N ) P(v |N ) and explore the sensitivity of name embeddings under dierent f , l ) for frequent and rare names in next subsection. parameters. e parameters that we test include: (i) dierent em- We can get Equ. 1 by using Bayesian rule under the assumption v v N bedding learning method: CBOW (Continuous Bag Of Word) or SG that f and l are conditionally independent given . (Skip-Gram); (ii) use joint embedding space of rst/last names or 4.1.2 Parameter Estimation. We estimate name part likelihood separate; (iii) number of nearest neighbor. from 4 sources: (i) training data, i.e. the names appear in training We can see from Tab. 1 that the joint variants generally perform data (denoted as Vtr ); (ii) name embedding, the names from contact best. However the dierences between the variants are relatively lists that have embeddings (Vem); (iii) prex/sux strings, names small. In addition, the CBOW model generally outperforms the SG that share the same prex/sux with names in training data (Vp/s ); model. It seems P1(B|B) is relatively low (0.35-0.59). However, it (iv) name characters, names that use the same language characters is essentially a harder task to nd a black name because a random (e.g. Arabic) seen in training data (Vch). Intuitively, the increasing name from the contact lists has a probability of 0.03 being Black, order of vocabulary size is Vtr , Vem, Vp/s , Vch, which is also the while 0.74 being White. decreasing order of estimation condence. 4 NATIONALITY CLASSIFICATION Training Data. Eq. 2 shows the most eective and simple way to estimate P(v |N ) and P(v |N ) directly from training data. 4.1 Methodology l f NamePrism uses Naive Bayes model because of its eectiveness C(vi , N ) Ptr (vi |N ) = ,vi ∈ Vtr (2) and interpretability. We argue that name nationalities depend on C(N ) both rst name and last name. is is especially eective for names where vi is either a rst name or last name from Vtr . C(vi , N ) used across dierent nationalities but with dierent popularities. is the count of vi with nationality N and C(N ) is equivalent to Í It also helps to reduce errors when names are mixtures because v C(v, N ). Note that each name part in Vtr have more than 5 of immigration or cross-nationality marriages. We put much ef- occurrences in training data so that we have high condence in the fort on estimating parameters, i.e. name parts likelihood, using estimation. features from training data, name embedding, substrings and string characters. erefore, each parameter has at most 4 estimations. Name Embedding. e likelihood of names (vi ) in Vem can be NamePrism uses the ones with largest condence for predictions. estimated using k-NN, i.e. take the average of k nearest neighbors (e.g. kNNs) in Vtr . However, we did not directly estimate the 4.1.1 Naive Bayes Model. In many case, our last names reveal likelihood using its kNNs’ likelihood. Instead, we realize that it our nationality origins. For example, “Zhang” is a common Chinese performs beer if we rst estimatevi ’s posterior using its neighbors’ posteriors and then apply Bayes rule to estimate the likelihood (Eq. Algorithm 1: NamePrism , a hierarchical nationality classier 3). It makes sense because names with similar embeddings do not Input :rst/last name vf , vl ; nationality taxonomy; necessarily have similar popularity (see Fig. 3). e estimation of estimated parameter sets Ptr , Pem, Pp/s , Pch. Pem(vi |N ) is formulated by Eq. 3 and 4. Output:nationality prediction T 1 Init T = root class ; Pem(N |vi )P(vi ) P (v |N ) = ,v ∈ V (3) 2 while T is not a leaf class do em i P(N ) i em 3 for child class Ni of T do 4 for each name part v ∈ {v ,v } do 1 Õ f l Pem(N |vi ) = Ptr (N |vj ) (4) 5 if v ∈ Vtr then |kN N (vi )| vj ∈k N N (vi ) 6 P(v|Ni ) = Ptr (v|Ni ); 7 else if v ∈ Vem then where kN N (vi ) is the set of name parts that are vi ’ kNNs. 8 P(v|Ni ) = Pem(v|Ni ); Prex/Sux Strings. As mentioned in [2], prex and sux of 9 else name parts are indicative features. For name part vi ∈ Vp/s , we 10 P(v|Ni ) = σ;# σ is a small constant can estimate its likelihood by averaging the ones’ which share the same prex/sux. 11 if neither of vf ,vl in Vtr or Vem then 12 for child class Ni of T do ∈ { } 1 Õ 13 for each name part v vf ,vl do ( | ) ( | ) ∈ Pp/s vi N = Ptr vj N ,vi Vp/s (5) 14 if v ∈ Vp/s then |PS(vi )| v ∈PS(v ) j i 15 P(v|Ni ) = Pp/s (v|Ni ); 16 else if v ∈ V then where PS(vi ) is the set of prex and sux strings of vi . Here we use ch 17 P(v|Ni ) = Pch(v|Ni ); substrings with length between 3 to 5. Pp/s (vj |N ) is the average likelihood of name parts in Vtr that have prex/sux vj . 18 P(Ni |vf ,vl ) ∝ P(vf |Ni )· P(vl |Ni )· P(Ni ); Name Characters. If a name is so rare that it is not in Vtr nor 19 T = arg max P(Ni |vf ,vl ); Ni Vem. Moreover, it doesn’t contain valid prex or sux strings. For example, a name wrien in “Hangul”, “HK” . It is very likely to be 20 return T ; a Korean name because most names in “Hangul” are Korean names. erefore, for a name vi ∈ Vch, we use the average of names in same characters to estimate its likelihood.
N N N I I W N s 1 Õ PI (N ) = = W = PW (N ) N (7) Pch(vi |N ) = Ptr (vj |N ),vi ∈ Vch (6) Í N Í N Í N |CH(v )| I W N I S i N N Í N vj ∈CH (vi ) N W where CH(vi ) is the set of names in training data that are wrien where S is the overall sample ratio and sN is the sample ratio of I in the same language as vi . N . P (N ) can be estimated from training data. sN and S can be computed by looking up countries’ populations. We can put Eq. 7 4.1.3 Internet Population vs. World Population. As we have into Eq. 1 when classifying names from world population. shown in previous subsections, the name parts likelihood are esti- mated from email/Twier users. However, Internet services (Email 4.1.4 Hierarchical Classification. Names are classied on a pre- and Twier) has varying popularity in dierent countries. ere- dened taxonomy in top-down fashion (see Fig. 5). e detailed fore, we need to assign dierent priors if a name is not sampled algorithm are shown in Alg. 1. We start from root class of the tax- from Internet users. For example, UK and South Africa have similar onomy (line 1). In each iteration, it picks the class that maximizes population (around 50M to 60M). In our datasets, we have an order P(Ni |vf ,vl ) (from line 2 to 19) until it meets a leaf class. Since we of magnitudes more names from the UK than from South Africa. have higher condence in Ptr than Pem, so we prefer parameters erefore we need to adjust to the real population of countries from the former (line 3 to 10). If neither of the name parts are in when we are predicting a random name from the world population. Ptr or Pem, we use the parameters from Pp/s or Pch (line 11 to 17). Formally, let PI (·) be probabilities over Internet population, Note that if only one of the name part in Ptr or Pem, we will only and PW (·) be the probability over world population. We have use the partial signal and smooth the other name part. I W P (vi |N ) = P (vi |N ) by assuming that names of Internet popula- tion are random samples from corresponding countries. Let I N be 4.2 Nationality Taxonomy Construction the number of names with nationality N on Internet population and e nationality taxonomy is a key component in our method. Ma- N I I N W be the one of N on world population. us, P (N ) = Í N teos et al. proposed a nationality taxonomy based on Cultural, N I W W N I Ethnic and Linguist (CEL) similarities [20]. Our name-based na- and P (N ) = Í N . We can get the relation between P (N ) and N W tionality taxonomy is constructed on top of CEL-based taxonomy, PW (N ) with Eq. 7. especially for the top level construction. While there is no “gold World Muslim African Hispanic Turk Maghreb: Nubian: WestAfrican: EastAfrican: Spanish: Tunisia- Egypt- Congo-Liberia-Senegal- Zambia-Rwanda- Dominican Republic-Uruguay-Guatemala- CentralAsian: Turkey Morocco- Somalia- Sierra Leone-Guinea- Tanzania-Kenya- Colombia-Venezuela-Chile-Panama- Kazakhstan-Azerbaijan- Algeria- Sudan Ghana-Togo-Benin- South Sudan- Bolivia-El Salvador-Ecuador-Argentina- Uzbekistan Libya Nigeria Ethiopia-Uganda Honduras-Peru-Costa Rica-Paraguay- Mexico-Spain-Nicaragua ArabianPeninsula: Pakistanis Persian: SouthAfrican: United Arab Emirates-Iraq-Yemen- Bangladesh Iran- Botswana-Namibia-Zimbabwe-South Africa Portuguese: Philippines Bahrain-Syria-Jordan-Oman- Afghanistan Portual-Brazil-Angola- Lebanon-Qatar-Kuwait- Pakistan EastAsian Mozambique Saudi Arabia Indochina Malay Nordic CelticEnglish: Ireland- Scandinavian European Cambodia Myanmar Indonesia United Kingdom EastEuropean: German: Baltics: Russian: Denmark Czech Republican-Slovakia- Germany- Latvia- Russia- Thailand Vietnam Malaysia Jewish: Hungry-Poland Austria- Estonia- Belarus- Sweden Norway Israel Netherland- Lithuania Ukraine Chinese: South Korea SouthSlavs: Switzerland Singapore-Hong Kong- Finland Bosnia and Herzegovina- Italian China-Taiwan Greek: Serbia-Slovenia-Montenegro- French: Japan Greece Bulgaria-Croatia-Macedonia Romania Italy SouthAsian: Belgium-France Nepal-India-Sri Lanka
Figure 5: Treemap of nationality taxonomy. Nested blocks within a larger block are its child nodes. 118 countries/regions, covering over 90% world population, are assigned to 39 leaf nationalities. e taxonomy is constructed based on Cultural, Ethnic and Linguist (CEL) similarities.
Congo Sudan Ethiopia parts distributions. ese similarities are helpful to construct the Togo Libya boom levels of the taxonomy. For example, Hispanic countries Cameroon Somalia Egypt are divided into three subgroups: Spanish, Portuguese and Philip- pines. e reason is, countries within Spanish and Portuguese are
Morocco very similar to each other according to name similarities, indicting Benin ner-granularity groupings are not feasible and necessary. Angola Algeria
Ghana Mozambique Nigeria 4.3 Datasets Tunisia Botswana 4.3.1 Name Labels. In order to estimate parameters mentioned
Uganda Zimbabwe above, we need name labels, i.e. full name and nationality pairs. We Rwanda collected 68M such pairs from the Email source and 6M pairs from South Africa Kenya Twier, totaling 74M labeled names from 118 major countries (Fig. 5). ese countries take up over 90% of world population. To remove Tanzania noise, we lter out names where both parts appear only once. 91% South Sudan Zambia Namibia names remain. Note that we are interested in nationalities, thus immigration countries, including U.S., Canada and Australia, are Liberia Guinea not included in our dataset. To preserve privacy for the email data, Senegal the IDs of these users (e.g. email address) are removed. Furthermore, Sierra Leone we only retain the counts of rst/last names and countries. We used the full name and country labels solely for the purpose of Figure 6: Name similarities between countries of Africa us- performance measurement. ey are not retained for classication. ing Email/Twitter data. icker edges indicate stronger sim- ilarities and more common rst/last names usages. East- Email. 90% of name labels come from email. Each name part ern African countries in green, western in purple, northern appears at least twice so that typos and random strings are ltered. in orange and southern in red. e clusters of same-color Note that the email contact lists and labeled names are dierent nodes indicate that countries with similar names tend to be set of users. We set 5 as thresholds for both Vtr and Vem. It turns | | | | close geographically. out Vtr is 1.02M and Vem is 4.09M. It makes sense because con- tact lists are names from many email companies and thus a larger population. standard” name-based nationality taxonomy because of the com- Twier. Although the email data oers the majority of name plexity in naming customs around the world, we consult opinions labels, its imbalanced popularity across the world make some re- from linguists and people from dierent cultures to reach a com- gions inadequate name labels. We noticed that Twier5, as an mon ground as a useful approximation . Moreover, as shown in Sec. 4.3.2, we could compute similarities between countries using name 5Twier API: hps://dev.twier.com/rest/public Wikipedia Data Email/Twier Data Nationality Name# HMM Ethnea Embd Prism Prismw Name# HMM Ethnea Embd Prism Prismw GreaterAfrican 11K 0.428 0.532 0.480 0.543 0.486 31K 0.269 0.389 0.554 0.645 0.622 GreaterEuropean 113K 0.863 0.903 0.927 0.932 0.899 225K 0.725 0.815 0.861 0.920 0.902 Asian 24K 0.654 0.670 0.711 0.745 0.748 123K 0.674 0.709 0.763 0.910 0.904 Muslim* 7K 0.380 0.563 0.538 0.615 0.611 13K 0.204 0.374 0.602 0.612 0.533 Africans* 4K 0.285 0.268 0.282 0.314 0.259 18K 0.174 0.288 0.458 0.636 0.659 WestEuropean 49K 0.631 0.724 0.709 0.747 0.756 143K 0.553 0.735 0.780 0.873 0.878 EastEuropean* 9K 0.488 0.517 0.466 0.575 0.629 38K 0.301 0.582 0.726 0.794 0.812 British* 44K 0.611 0.760 0.789 0.794 0.768 35K 0.361 0.578 0.627 0.648 0.689 Jewish* 11K 0.313 0.111 0.095 0.129 0.183 9K 0.097 0.361 0.301 0.405 0.387 GreaterEastAsian 15K 0.637 0.626 0.642 0.690 0.706 97K 0.625 0.656 0.713 0.907 0.895 IndianSubContinent* 9K 0.523 0.660 0.768 0.769 0.746 26K 0.438 0.721 0.855 0.912 0.903 Italian* 14K 0.521 0.543 0.595 0.634 0.613 11K 0.233 0.453 0.665 0.713 0.763 Hispanic* 11K 0.403 0.600 0.397 0.521 0.538 69K 0.432 0.724 0.676 0.850 0.864 Nordic* 5K 0.400 0.587 0.713 0.709 0.709 23K 0.303 0.653 0.767 0.783 0.783 French* 14K 0.428 0.523 0.602 0.600 0.624 27K 0.203 0.426 0.738 0.769 0.750 Germanic* 5K 0.254 0.410 0.401 0.403 0.412 13K 0.140 0.431 0.582 0.629 0.653 Japanese* 8K 0.646 0.724 0.456 0.547 0.695 57K 0.674 0.788 0.434 0.928 0.939 EastAsian* 7K 0.499 0.455 0.609 0.621 0.549 40K 0.270 0.340 0.723 0.834 0.811 Weighted Avg. — 0.492 0.607 0.619 0.648 0.651 — 0.364 0.580 0.642 0.790 0.795 Table 2: F1 scores on a 13-leaf taxonomy. Existing methods: HMM [2] and Ethnea [28]; Embd only uses parameters from name embeddings; Prismw is NamePrism with world population as priors. Nationalities on dierent levels of taxonomy are separated with bold lines. ‘*’ marks leaf nationalities. Weighted Avg. is count-weighted average F1 of leaf nationalities. emerging Web service, has a wider coverage and thus can act as a labels are colored in accordance with the map. It is apparent that supplementary source of name labels. countries with same colors are clustered, indicating that nearby In order to get name labels from interested regions, we (i) get list countries have similar names. One interesting case is that Angola is of most popular regional celebrities6; (ii) get all followers’ Twier connected with Mozambique, even though one is on the west coast proles of the celebrities’. Each prole record contains “name” and of the continent while the other is on the east coast. e reason is “location” elds, though many users leave the laer blank. In sum- that both countries were once colonized by Portuguese, thus many mary, we gathered 43M unique Twier user proles, within which Internet users have Portuguese names. 9M have non-empty “location” eld and well-formed names (e.g. We compute the similarities between countries with following two name parts and string length > 1). However, these location tags steps: (i) aggregate name parts of each country so that countries are are not well dened. Among 9M proles, there are ∼1.5M unique represented by name part vectors, where each dimension indicates locations. Some of them are simply noise, while some oer too how many name parts occur in the countries, (ii) compute cosine much details (e.g. university name without country info.). ere- similarity between vectors, i.e. name similarities between countries. fore, we use Google Map API7 to query for country names using Note that in Fig. 6, the thickness of edges indicate the magnitude the 10% most popular “locations”. As a result, we have ∼6M labeled of similarities. One link is made if either the similarity is larger names for use, supplementary to the labels from email source. than 0.5 or it makes sure that each country is linked to at least one most similar countries. erefore, Ethiopia is linked to Sudan with 4.3.2 Name Similarities between countries. Since our labeled a very small weight, though it is distinct from other countries. names are collected from Internet, it is important to check its quality. In this subsection, we provide an interesting perspective to validate 4.4 Performance Evaluation the high quality of the datasets. We compute the similarities between countries using the aggre- In this Subsection, we will rst compare our method with existing gations of names, and check whether they agree with common systems on smaller nationality taxonomies (one 13-leaf taxonomy sense. In fact, we observe that the cultural/spatial closeness be- and one 10-leaf at taxonomy [2, 28, 29]). Note we use their Web tween countries are well captured by country name similarities. APIs to collect the classication results. Two independent datasets Take African continent as an example (shown in Fig. 6). On the are tested on. e smaller one is from Wikipedia (used in [2, 29]), right-boom part of the gure, the continent map is divided into 4 the other is from our test set of labeled names. In the end, we major parts based on how close they are culturally and geograph- will introduce more details about NamePrism’s performance on a ically. On the remaining part of the gure, countries with names ner-grained nationality taxonomy.
6hps://www.socialbakers.com/statistics/twier/proles/kenya/ 4.4.1 On Small Taxonomy. Ambekar et al. proposed an HMM- 7hps://developers.google.com/maps/ based method, which used signals from substrings of names [2] Wikipedia Email/Twier Nationality Name# Prism Nationality Name# Prism Nationality Name# Seer Prism Name# Seer Prism CelticEnglish* 3505K 0.725 SouthAsian* 2623K 0.890 Muslim 7K 0.560 0.646 13K 0.422 0.688 Jewish* 11K 0.396 African 606K 0.589 EastEuropean 9K 0.739 0.596 38K 0.343 0.804 Muslim 1475K 0.741 EastAsian 6157K 0.920 British 44K 0.852 0.843 35K 0.577 0.726 Greek* 259K 0.887 Hispanic 6892K 0.907 Indian 9K 0.768 0.779 26K 0.639 0.880 Nordic 195K 0.731 European 5371K 0.836 Hispanic 11K 0.605 0.558 69K 0.610 0.871 Nubian* 577K 0.650 Japan* 65K 0.836 Germanic 5K 0.464 0.487 13K 0.433 0.694 Maghreb* 47K 0.148 Malay 2596K 0.863 French 14K 0.676 0.650 27K 0.482 0.802 ArabPeninsula* 172K 0.510 Chinese* 2901K 0.928 Italian 14K 0.707 0.641 11K 0.329 0.728 Turkic 78K 0.676 Portuguese* 2683K 0.886 EastAsian 7K 0.824 0.635 40K 0.418 0.848 Pakistanis 179K 0.511 Philippines* 1137K 0.724 Japanese 8K 0.875 0.550 57K 0.902 0.929 Persian* 423K 0.656 Spanish* 3072K 0.851 Weighted Avg. — 0.751 0.700 — 0.571 0.831 Finland* 30K 0.739 German* 1278K 0.739 Table 3: F1 scores on 10 nationalities. EthnicSeer[29] per- Scandinavian 165K 0.704 Baltics* 12K 0.408 forms slightly better on Wikipedia data but it is an un- WestAfrican* 315K 0.563 French* 2674K 0.825 fair comparison because it is trained on the same dataset. SouthAfrican* 66K 0.370 Russian* 121K 0.716 EastAfrican* 225K 0.574 EastEurope* 65K 0.492 NamePrism performs signicantly better on a larger test set SouthKorea* 68K 0.861 SouthSlavs* 68K 0.570 from Email/Twitter. Indochina 528K 0.901 Italian 1153K 0.745 CentralAsian* 3K 0.196 Cambodia* 1K 0.162 Turkey* 75K 0.687 Vietnam* 502K 0.913 to classify name nationalities. eir taxonomy contains 13 leaf Bangladesh* 78K 0.578 ailand* 18K 0.592 nodes and 18 nodes in total (see [2] for the denition of this tax- Pakistan* 101K 0.449 Malaysia* 242K 0.480 onomy). In order to compare, all methods need to be on the same Denmark* 49K 0.662 Indonesia* 2354K 0.870 taxonomy. HMM is designed on this taxonomy. NamePrism and Sweden* 74K 0.607 Romania* 329K 0.663 Ethnea are adapted to this because both methods are dened on a Norway* 42K 0.620 Italy* 825K 0.710 ner-grained taxonomy. EthnicSeer is compared separately on a Myanmar* 7K 0.607 at 10-nationality taxonomy. Weighted Avg. — 0.806 Two datasets are available for comparison: (i) the labeled names Table 4: NamePrism performance (F1 scores) on a 39-leaf from Wikipedia (150K in total, the same dataset used to train HMM nationality taxonomy. Nationalities in dierent levels are and EthnicSeer); (ii) we divide Email/Twier data into training and separated with bolder lines. ‘*’ marks leaf nationalities. testing datasets (60% vs. 40%). en we sample 2% from the test Weighted Avg. is count-weighted average F1 of leaf nation- data for use because it is not ecient to get classication results of alities. baselines from their Web APIs (380K). Some small nationalities are given larger sampling ratio to get large enough test samples. NamePrism and NamePrismw ) are average F1 of 3 runs. e stan- As shown in Tab. 2, we compare results of ve methods: HMM dard deviations are all below 0.005. As we can see from Tab. 4, w [2], Ethnea [28], Embd, NamePrism and NamePrism . Embd only NamePrism performs well on most nationalities. For some less de- w use parameters estimated from name embeddings. NamePrism uses veloped countries with few Internet users, including Central Asian w the world population as priors. NamePrism and NamePrism per- countries and Maghreb countries, we have limited number of name forms best on most classes for both datasets. On Wikipedia data, labels and contact lists. us the performances on these nationali- our methods achieves best performances on 15 (out of 18) classes. ties are limited. To the best of our knowledge, our work is the rst Some classes get +10% F1 boost, including Indian, Nordic and East- eort trying to classify names belonging to these regions. Asian. On Email/Twier data, the improvement is more signicant. NamePrism outperforms the rest on all classes. Some classes get per- 5 ETHNICITY CLASSIFICATION formance increase by +30%, including Muslim, Africans, etc. Note As we have mentioned in Sec. 3, U.S. Census Bureau dened 6 that Embd also achieves considerable high performance, indicating race/ethnicity: White, Black, API, Hispanic, AIAN and 2PRACE. that name embedding is capturing nationality signals well. In order to build classier for these ethnicities, we need labeled EthnicSeer is dened on a 10-leaf at taxonomy. For comparison names for these ethnicities to estimate parameters. Fortunately, purpose, we removed the labeled names from African, Jewish and U.S. Census Bureau published ethnicity distribution for popular Nordic from both datasets. We also shrink NamePrism’s 39-leaf tax- last names. We can estimate rst names’ ethnicity distribution by onomy to t this small one. e weighted average F1 score shows connecting census labels with email names from the U.S. EthnicSeer performs slightly beer on Wikipedia but it is the same More formally, let V L be the set of popular last names from dataset that EthnicSeer is trained on. In contrast, NamePrism per- us Census Bureau, so we have ground truth, P (E|v ), v ∈ V L , forms signicantly beer on Email/Twier testing set. us l l us where E denote ethnicity. We can estimate the posteriors∀ of rst 4.4.2 On Large Taxonomy. Tab. 4 shows NamePrism F1 scores names with Eq. 8. on the large nationality taxonomy. Note we randomly split the 1 Õ Email/Twier data into training and testing sets (60% vs. 40%) for Pus (E|vf ) = P(E|vl ) (8) |S(vf )| 3 times. All reported performances of our methods (i.e. Embd, vl ∈S(vf ) (c) Singers (d) News (a) Athletes (b) Actors 15 30 300 100 Pop Soccer 250 10 Rap 20 Basketball 200 50 Cricket 5 10 150 Boxing 100 Tennis 0 0 0 50 5 10 0 50 Hollywood 10 20 Business 50 Bollywood Entertainment Over/Underpresentation(%) Over/Underpresentation(%) Over/Underpresentation(%) Over/Underpresentation(%) 100 100 15 30 White Black API Hispanic White Black API Hispanic White Black API Hispanic White Black API Hispanic
Figure 7: Ethnicity Over/underrepresentation of U.S. Twitter users’ interest on dierent topics: (a) Cricket is almost exclusively followed by Indians while soccer is more popular among Hispanics. (b) U.S. actors enjoy a more diverse popularity than Indian actors. (c) African-Americans like rap more than pop. (d) Asians follow business news more than entertainment.
Count 1400
20 1200 1000 Barack Obama 800 Hillary Clinton 600 0 Donald Trump 400 200 0 20 Over/Underrepresentation (%) White Black API Hispanic Figure 9: An Indonesian politician has 50% followers with British, Russian or Indian names while only 13% are Indone- Figure 8: Ethnicity Over/underrepresentation of Barack sians. Besides, his Twitter prole is also suspicious: 23K Obama, Hillary Clinton and Donald Trump’s U.S. Twitter Tweets but only 1 following. His tweets are written in In- followers. White followers are overrepresented for Trump, donesian, which most followers can not understand. Obama and Clinton have more followers among minorities.
where S(vf ) is the list of last names and (vf ,vl ) is a full name from and politicians; all of whom have from 1M to 100M followers. For U.S. email data. Note that some of the last names paired with vf each celebrity, we selected 50,000 random followers, and ltered may not have a ground truth label (i.e. v < V L ). To make reliable out accounts with irregular names using the same method as dis- l us e estimation, we only keep rst names that at least half of the paired cussed in 4.3.1). We then apply NamePrism and NamePrism to the last names with a ground truth label. erefore, we form a set of remaining followers. F Our primary observations here include: rst names (Vus ) with estimated ethnicity distributions. We denote F ∪ L ( | ) ( | ) Vus = Vus Vus . We can get Pus vf E and Pus vl E by applying • Ethnicity and the 2016 U.S. Presidential Election – ere has Bayes Rules. been considerable concern that the recent election exacer- For now, Vus can handle names with popular rst/last names. bated tensions between ethnic groups in the United States. For rare names, we can make use of the Email/Twier name labels. Indeed, our analysis of U.S.-based followers of the primary 118 countries are assigned to the six ethnicities based on their gures in the race (Obama, Clinton, and Trump) show stark denitions. For example, we make names from European countries dierences in composition. Fig. 8 shows that whites are as White while names from Asian as API. erefore, we can follow substantially overrepresented among Trump’s followers, similar steps as Algorithm 1. e dierence is we will rst check while Clinton and Obama have disproportionately more ( | ) whether a name part is from Vus . If yes, we will use Pus vi E to followers among minorities. ( | ) compute P E vf ,vl because they are estimated from ground truth • Interests and Ethnicity – Fig. 7 similarly breaks down the with high condence. Otherwise, we will then check whether they followers of major celebrities in sports, entertainment, and are in Vtr or Vem as in Algorithm 1 and follow the remaining steps. news categories. e followers of cricket and Bollywood stars are overwhelmingly Indian, while Hispanics dispro- 6 SOCIAL MEDIA ANALYSIS portionally favor soccer and boxing. Nationality and ethnicity classication have broad application in • Anomaly Detection through Nationality Analysis – We were sociological research and media analysis. Here we present some surprised to learn that an Indonesian politician named Jef- interesting observations, when we apply our classiers to the fol- frie Geovanie was one of the most heavily followed gures lowers of Twier celebrities. on Twier, because he has only 45K Google search results To collect data, we identied the 100 most followed celebrities in about him, mostly in Indonesian. Yet our name analysis of each of six categories: actors, singers, news, atheletes, governments his followers shows that only 13% are Indonesian, with over 50% of the followers of British, Russian, or Indian national- [11] Jonathan Chang, Itamar Rosenn, Lars Backstrom, and Cameron Marlow. 2010. ity (Fig. 9). is is quite peculiar given that Indonesian is ePluribus: Ethnicity on Social Networks. ICWSM 10, 18–25. [12] Andrew J Coldman, Terry Braun, and Richard P Gallagher. 1988. e classication the primary language of his Twier stream. of ethnic status using name information. 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