Emo, Love, and God: Making Sense of Urban Dictionary, a Crowd-Sourced Online Dictionary
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The Internet facilitates large-scale collaborative projects. 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 systematic malfunction and misbehavior. 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. There is also a high presence of offensive content, but highly offensive content tends to receive lower scores through the voting system. Our study highlights that Urban Dictionary has a higher content heterogeneity than found in traditional dictionaries, which poses challenges in terms in processing but also offers opportunities to analyze and track language innovation.
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