Comparison and benchmark of name-to-gender inference services

The increased interest in analyzing and explaining gender inequalities in tech, media, and academia highlights the need for accurate inference methods to predict a person’s gender from their name. Several such services exist that provide access to large databases of names, often enriched with inform...

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Bibliographic Details
Published in:PeerJ. Computer science Vol. 4; p. e156
Main Authors: Santamaría, Lucía, Mihaljević, Helena
Format: Journal Article
Language:English
Published: United States PeerJ, Inc 16.07.2018
PeerJ Inc
Subjects:
Accuracy
Algorithms
Authorship
Benchmarks
Bibliometrics
Classification
Classification algorithms
Computer supported cooperative work
Data Mining and Machine Learning
Data Science
Datasets
Digital Libraries
Digital media
Gender
Gender analysis
Inference
Inventors
Name-based gender inference
Names
Parameters
Performance evaluation
Performance measurement
Productivity
Scholarly publishing
Science
Scientometrics
Social networks
World Wide Web
New York
Germany
Performance evaluation
Scientometrics
Gender analysis
Classification algorithms
Name-based gender inference
Bibliometrics
ISSN:2376-5992, 2376-5992
Online Access:Get full text
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Summary:The increased interest in analyzing and explaining gender inequalities in tech, media, and academia highlights the need for accurate inference methods to predict a person’s gender from their name. Several such services exist that provide access to large databases of names, often enriched with information from social media profiles, culture-specific rules, and insights from sociolinguistics. We compare and benchmark five name-to-gender inference services by applying them to the classification of a test data set consisting of 7,076 manually labeled names. The compiled names are analyzed and characterized according to their geographical and cultural origin. We define a series of performance metrics to quantify various types of classification errors, and define a parameter tuning procedure to search for optimal values of the services’ free parameters. Finally, we perform benchmarks of all services under study regarding several scenarios where a particular metric is to be optimized.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.156