Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review
This review helps researchers and Wikipedia contributors by synthesizing existing work to improve automated quality assessment, though it is incremental as it summarizes rather than introduces new methods.
The authors conducted a systematic literature review of 149 studies on methods for automatically assessing the quality of Wikipedia articles, identifying and comparing machine learning algorithms, features, metrics, and datasets to address the challenge of manual quality assessment.
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.