  In this paper we present a benchmark dataset generated as part of a project
for automatic identification of misogyny within online content, which focuses
in particular on memes. The benchmark here described is composed of 800 memes
collected from the most popular social media platforms, such as Facebook,
Twitter, Instagram and Reddit, and consulting websites dedicated to collection
and creation of memes. To gather misogynistic memes, specific keywords that
refer to misogynistic content have been considered as search criterion,
considering different manifestations of hatred against women, such as body
shaming, stereotyping, objectification and violence. In parallel, memes with no
misogynist content have been manually downloaded from the same web sources.
Among all the collected memes, three domain experts have selected a dataset of
800 memes equally balanced between misogynistic and non-misogynistic ones. This
dataset has been validated through a crowdsourcing platform, involving 60
subjects for the labelling process, in order to collect three evaluations for
each instance. Two further binary labels have been collected from both the
experts and the crowdsourcing platform, for memes evaluated as misogynistic,
concerning aggressiveness and irony. Finally for each meme, the text has been
manually transcribed. The dataset provided is thus composed of the 800 memes,
the labels given by the experts and those obtained by the crowdsourcing
validation, and the transcribed texts. This data can be used to approach the
problem of automatic detection of misogynistic content on the Web relying on
both textual and visual cues, facing phenomenons that are growing every day
such as cybersexism and technology-facilitated violence.
