  The Everyday Sexism Project documents everyday examples of sexism reported by
volunteer contributors from all around the world. It collected 100,000 entries
in 13+ languages within the first 3 years of its existence. The content of
reports in various languages submitted to Everyday Sexism is a valuable source
of crowdsourced information with great potential for feminist and gender
studies. In this paper, we take a computational approach to analyze the content
of reports. We use topic-modelling techniques to extract emerging topics and
concepts from the reports, and to map the semantic relations between those
topics. The resulting picture closely resembles and adds to that arrived at
through qualitative analysis, showing that this form of topic modeling could be
useful for sifting through datasets that had not previously been subject to any
analysis. More precisely, we come up with a map of topics for two different
resolutions of our topic model and discuss the connection between the
identified topics. In the low resolution picture, for instance, we found Public
space/Street, Online, Work related/Office, Transport, School, Media harassment,
and Domestic abuse. Among these, the strongest connection is between Public
space/Street harassment and Domestic abuse and sexism in personal
relationships.The strength of the relationships between topics illustrates the
fluid and ubiquitous nature of sexism, with no single experience being
unrelated to another.
