What reading 3.5 million books tells us about gender stereotypes

Huge social questions like "how are men and women perceived differently" cannot be easily answered without analyzing rhetoric on a massive scale. But what if we could analyze millions of words, all at once, to get a sense of what patterns emerge in how men and women were described? It wasn't until recently that machine learning algorithms could help researchers do just that. In a recent study, Dr. Isabelle Augenstein, a computer scientist at the University of Copenhagen, worked with fellow researchers from the United States to analyze 11 billion words in an effort to find out whether there was a difference between the adjectives used to describe men and women in literature. The researchers examined a dataset of 3.5 million books, all published in English between 1900 to 2008.