2 October 2019

World Economic Forum: This machine read 3.5 million books then told us what it thought about men and women

Researchers trawled through an enormous quantity of books in an effort to find out whether there is a difference between the types of words that describe men and women in literature. Using a new computer model, the researchers analyzed a dataset of 3.5 million books, all published in English between 1900 to 2008. The books include a mix of fiction and non-fiction literature. [...]

Their analysis demonstrates that negative verbs associated with body and appearance appear five times as often for female figures as male ones. The analysis also demonstrates that positive and neutral adjectives relating to the body and appearance occur approximately twice as often in descriptions of female figures, while male ones are most frequently described using adjectives that refer to their behavior and personal qualities.[...]

The researchers point out that the analysis has its limitations, in that it does not take into account who wrote the individual passages and the differences in the degrees of bias depending on whether the books were published during an earlier or later period within the data set timeline. Furthermore, it does not distinguish between genres—e.g. between romance novels and non-fiction. The researchers are currently following up on several of these items.

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