Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary
This work addresses the quality and usability of crowd-sourced dictionaries for researchers and linguists, though it is incremental as it builds on existing studies of online platforms.
The authors analyzed Urban Dictionary, a crowd-sourced online dictionary, finding that it contains a high proportion of opinion-focused entries and informal content, with offensive material often receiving lower scores through voting, but its heterogeneous nature poses challenges for studying language innovation.
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.