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.
Keywords
Cite
@article{arxiv.2310.02235,
title = {Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review},
author = {Pedro Miguel Moás and Carla Teixeira Lopes},
journal= {arXiv preprint arXiv:2310.02235},
year = {2023}
}
Comments
37 pages, 10 figures, just accepted in ACM Computing Surveys (September 2023). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Computing Surveys, https://dx.doi.org/10.1145/3625286