Irvilab

Arvola, Paavo; Alamettälä, Tuulikki

doi:10.1145/3477495.3531662

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Wikipedia has been referred to as the "Web 2.0 Tower of Babel" (Hecht and Gergle 2010) in which knowledge is created and maintained by volunteers in more than 300 language versions, each independently. Therefore, some resources that have been made available to support information retrieval tasks in multiple languages also rely on Wikipedia (Dietz et al 2022;Srinivasan et al 2021;Arvola and Alamettälä 2022;Yang et al 2022).…”

Section: Language-agnostic Approachesmentioning

confidence: 99%

Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages

Das,

Johnson,

Saez-Trumper

et al. 2024

ICWSM

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Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use.

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