Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Journal of the American Society for Information Science and Technology
Minimally supervised induction of grammatical gender
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Baby Names, Visualization, and Social Data Analysis
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Proceedings of the 18th international conference on World wide web
Author name disambiguation in MEDLINE
ACM Transactions on Knowledge Discovery from Data (TKDD)
Glen, Glenda or Glendale: unsupervised and semi-supervised learning of English noun gender
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Inferring gender of movie reviewers: exploiting writing style, content and metadata
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
What's in a name: a study of names, gender inference, and gender behavior in facebook
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
He said, she said: gender in the ACL anthology
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
| Hi-index | 0.00 |
This paper presents an approach for predicting the gender orientation of any given first name over time based on a set of search engine queries with the name prefixed by masculine and feminine markers (e.g., "Uncle Taylor"). We hypothesize that these markers can capture the great majority of variability in gender orientation, including temporal changes. To test this hypothesis, we train a logistic regression model, with time-varying marker weights, using marker counts from Bing.com to predict male/female counts for 85,406 names in US Social Security Administration (SSA) data during 1880-2008. The model misclassifies 2.25% of the people in the SSA dataset (slightly worse than the 1.74% pure error rate) and provides accurate predictions for names beyond the SSA. The misclassification rate is higher in recent years (due to increasing name diversity), for general English words (e.g., Will), for names from certain countries (e.g., China), and for rare names. However, the model tends to err on the side of caution by predicting neutral/unknown rather than false positive. As hypothesized, the markers also capture temporal patterns of androgyny. For example, Daughter is a stronger female predictor for recent years while Grandfather is a stronger male predictor around the turn of the 20th century. The model has been implemented as a web-tool called Genni (available via http://abel.lis.illinois.edu/) that displays the predicted proportion of females vs. males over time for any given name. This should be a valuable resource for those who utilize names in order to discern gender on a large scale, e.g., to study the roles of gender and diversity in scholarly work based on digital libraries and bibliographic databases where the authors? names are listed.