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

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

URL: http://arxiv.org/abs/2404.09764v1
Date: Mon, 15 Apr 2024 13:07:31 GMT
Title: Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages
Authors: Paramita Das, Isaac Johnson, Diego Saez-Trumper, Pablo Aragón,
Abstract summary: We propose a novel computational framework for modeling the quality of Wikipedia articles. Our framework is based on language-agnostic structural features extracted from the articles. We have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia.
Score: 0.19698344608599344
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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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