  The Internet facilitates large-scale collaborative projects and the emergence
of Web 2.0 platforms, where producers and consumers of content unify, has
drastically changed the information market. On the one hand, the promise of the
"wisdom of the crowd" has inspired successful projects such as Wikipedia, which
has become the primary source of crowd-based information in many languages. On
the other hand, the decentralized and often un-monitored environment of such
projects may make them susceptible to low quality content. In this work, we
focus on Urban Dictionary, a crowd-sourced online dictionary. We combine
computational methods with qualitative annotation and shed light on the overall
features of Urban Dictionary in terms of growth, coverage and types of content.
We measure a high presence of opinion-focused entries, as opposed to the
meaning-focused entries that we expect from traditional dictionaries.
Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as
proper nouns. Urban Dictionary also contains offensive content, but highly
offensive content tends to receive lower scores through the dictionary's voting
system. The low threshold to include new material in Urban Dictionary enables
quick recording of new words and new meanings, but the resulting heterogeneous
content can pose challenges in using Urban Dictionary as a source to study
language innovation.
