Automatic Quality Assessment of Wikipedia Articles—A Systematic Literature Review

Abstract

Wikipedia is the world’s largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality metrics, and used datasets, examining 149 distinct studies, and exploring commonalities and gaps in them. The literature is extensive, and the approaches follow past technological trends. However, machine learning is still not widely used by Wikipedia, and we hope that our analysis helps future researchers change that reality.

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

“…Maintaining the quality of content in parallel with its rapid evolution is a both crucial and overwhelming task in large peer-to-peer production systems such as Wikipedia. Due to the immense cost involved in the process of monitoring the quality of articles in Wikipedia, several works have proposed different algorithmic methods for automatic quality assessment (Moás and Lopes 2023). The most intuitive approach is to provide support with machine learning supervised models trained on the basis of domain-specific feature engineering (Warncke- Wang, Cosley, and Riedl 2013;Dang and Ignat 2016;Bassani and Viviani 2019).…”