1. more matildas
katherine yoshida

2. Central Characters in
Children’s Books
Gender in 20th century children’s books (McCabe et al., 2011)

4. morematildas.com

5. User
Query
Term
Ranking
Gender
Ranking
Overall
Ranking

6. Term
Ranking
Gender
Ranking
Overall
Ranking
Tokenize & remove
irrelevant information
Lowercase, lemmatize,
stopword removal
Terms database
User
Query

7. User
Query
Term
Ranking
Gender
Ranking
Overall
Ranking
Genderize proper names
Count pronouns
e.g., her, his, she, he
Social Security DB of names
Naive Bayes classifier
F (PNs + pronouns) - M (PNs + pronouns)
length of description

8. Katherine Yoshida
▷ PhD in cognitive science (UBC)
▷ UX research & consulting

9. thank you!

10. Stack
MySQLPython
• NLTK
AWS

11. Gendered pronouns
female +1: she, her, hers
male +1: he, his, him
Naive Bayes classifier
Proper names
NLTK entities bigram parser (‘people’)
Social Security database of names
Naive Bayes classifier for other names

12. Social security database: Vast majority of proper names
Three features:
● Last letter of name
● Last two letters of name
● Is last letter a vowel (aeiouy)
Trained on 80% of names from Social Security names
database
Validated on 20% holdout sample
Accuracy = 80%
Naive Bayes classifier

13. Average Gender Counts
Female Male
Pronouns 4.7 5.1
Proper Names 2.8 4.6
Total 7.5 9.7

14. Frequency x Female proportion