  Wikipedia is a community-created encyclopedia that contains information about
notable people from different countries, epochs and disciplines and aims to
document the world's knowledge from a neutral point of view. However, the
narrow diversity of the Wikipedia editor community has the potential to
introduce systemic biases such as gender biases into the content of Wikipedia.
In this paper we aim to tackle a sub problem of this larger challenge by
presenting and applying a computational method for assessing gender bias on
Wikipedia along multiple dimensions. We find that while women on Wikipedia are
covered and featured well in many Wikipedia language editions, the way women
are portrayed starkly differs from the way men are portrayed. We hope our work
contributes to increasing awareness about gender biases online, and in
particular to raising attention to the different levels in which gender biases
can manifest themselves on the web.
