Growing Wikipedia Across Languages via Recommendation

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Growing Wikipedia Across Languages via Recommendation - scientific work related to Wikipedia quality published in 2016, written by Ellery Wulczyn, Robert West, Leila Zia and Jure Leskovec.

Overview

The different Wikipedia language editions vary dramatically in how comprehensive they are. As a result, most language editions contain only a small fraction of the sum of information that exists across all Wikipedias. In this paper, authors present an approach to filling gaps in article coverage across different Wikipedia editions. Authors main contribution is an end-to-end system for recommending articles for creation that exist in one language but are missing in an- other. The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests. Authors empirically validate models in a controlled experiment involving 12,000 French Wikipedia editors. Authors find that personalizing recommendations increases editor engagement by a factor of two. Moreover, recommending articles increases their chance of being created by a factor of 3.2. Finally, articles created as a result of recommendations are of comparable quality to organically created articles. Overall, system leads to more engaged editors and faster growth of Wikipedia with no effect on its quality.