  Norms are challenging to define and measure, but this paper takes advantage
of text data and the recent development in machine learning to create an
encompassing measure of norms. An LSTM neural network is trained to detect
gendered language. The network functions as a tool to create a measure on how
gender norms changes in relation to the Metoo movement on Swedish Twitter. This
paper shows that gender norms on average are less salient half a year after the
date of the first appearance of the hashtag #Metoo. Previous literature
suggests that gender norms change over generations, but the current result
suggests that norms can change in the short run.
