Full Citation
Title: Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods
Citation Type: Conference Paper
Publication Year: 2016
ISBN: 9781450341448
ISSN:
DOI: 10.1145/2872518.2889385
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Abstract: Computational social scientists often harness the Web as a "societal observatory" where data about human social behavior is collected. This data enables novel investigations of psychological, anthropological and sociological research questions. However, in the absence of demographic information, such as gender, many relevant research questions cannot be addressed. To tackle this problem, researchers often rely on automated methods to infer gender from name information provided on the web. However, little is known about the accuracy of existing gender-detection methods and how biased they are against certain sub-populations. In this paper, we address this question by systematically comparing several gender detection methods on a random sample of scientists for whom we know their full name, their gender and the country of their workplace. We further suggest a novel method that employs web-based image retrieval and gender recognition in facial images in order to augment name-based approaches. Our findings show that the performance of name-based gender detection approaches can be biased towards countries of origin and such biases can be reduced by combining name-based an image-based gender detection methods.
Url: http://dl.acm.org/citation.cfm?doid=2872518.2889385
User Submitted?: No
Authors: Karimi, Fariba; Wagner, Claudia; Lemmerich, Florian; Jadidi, Mohsen; Strohmaier, Markus
Conference Name: Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion
Publisher Location: Montréal, Québec, Canada
Data Collections: IPUMS USA
Topics: Gender, Methodology and Data Collection
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