The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Comparison and benchmark of name-to-gender inference services
Open accessAbstract
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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Metadata
- Authors
- Santamaría, L., & Mihaljević, H.
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2018-07-16
- Volume
- 4
- Pages
- e156
- ISSN
- 2376-5992 (ISSN-L)
- OpenAlex
- W2883284975
- Cited by
- 401
- References
- 17
- FWCI
- 16.13
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
