Emo, love and god: making sense of Urban Dictionary, a crowd-sourced online dictionary

Abstract

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 unmonitored 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.

Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity

“…Wilson et al [22] used UD as a training corpus for neural-network based word embeddings, finding that these embeddings were competitive with other popular pretrained word embeddings models across a range of tasks including sentiment analysis and sarcasm detection. Closest to our work is that by Nguyen et al [17], who performed a quantitative study of terminology indexed on UD. They offer a statistical analysis of UD's content, showing for example a high presence of opinion-focused entries.…”

“…Urban Dictionary is an online, crowd-sourced dictionary for (mostly) 3 English-language terms containing definitions that are not typically captured by traditional dictionaries. In the best cases, users provide meaningful definitions for new and emerging language, while in reality, many entries are a mix of honest definitions ("Stan: a crazy or obsessed fan"), jokes ("Shoes: houses for your feet"), personal messages ("Sam: a really kind and caring person"), and inappropriate or offensive language [18]. Each entry, uploaded by a single user, contains a term, its definition, examples, and tags ( Figure 2).…”

“…In this study, we focus on the platform Urban Dictionary (UD), 1 which is an online, crowdsourced dictionary for English slang and colloquial language. Urban dictionary is known to be both complex and noisy, but also potentially invaluable in terms of its vantage of emerging slang terminology [17]. It serves as a mirror of parts of today's society, reflecting current trends and providing a perspective on the zeitgeist.…”

Neologising misogyny: Urban Dictionary’s folksonomies of sexual abuse

“…2 At the time of writing, web analytics company Quantcast ranked it as the 22nd most visited site in the United States, garnering approximately 34 million visitors per month. 3 Urban Dictionary has been consulted when building a text normalisation system for Twitter and has been used to create training data for a Twitter-specific sentiment lexicon (Nguyen et al, 2017). It has also been consulted in several high-profile court cases in the United States (Kaufman, 2013).…”

“…Unlike other online dictionaries such as Wiktionary or Merriam-Webster, which undergo much deeper levels of curation (Nguyen et al, 2017), Urban Dictionary does not have inclusion criteria based on usage frequency and does not trawl vast quantities of Internet content for context in order to generate the most popular examples of usage. Merriam-Webster editors, for example, look at news stories, books and menus in search of new words and constantly run lists of how words are used and how often they are used.…”