Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics





Version 1 : Received: 10 May 2019 / Approved: 13 May 2019 / Online: 13 May 2019 (08:10:17 CEST) Version 2 : Received: 2 August 2019 / Approved: 5 August 2019 / Online: 5 August 2019 (12:26:34 CEST)

A peer-reviewed article of this Preprint also exists. Lewoniewski, W.; Węcel, K.; Abramowicz, W. Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics. Computers 2019, 8, 60. Lewoniewski, W.; Węcel, K.; Abramowicz, W. Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics. Computers 2019, 8, 60. Copy Journal reference: Computers 2019, 8, 60

DOI: 10.3390/computers8030060

Cite as: Lewoniewski, W.; Węcel, K.; Abramowicz, W. Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics. Computers 2019, 8, 60. Lewoniewski, W.; Węcel, K.; Abramowicz, W. Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics. Computers 2019, 8, 60. Copy CANCEL COPY CITATION DETAILS

Abstract

In Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, quality of information about the same topic depends on language. Any interested user can improve an article and that improvement may depend on popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper we also analyze how popular are selected topics among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we divided articles into 27 main topics based on information extracted from over 10 million categories in 55 language versions and analyzed about 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content.

Subject Areas

Wikipedia; Information quality; popularity; topics identification; Wikidata; DBpedia; WikiRank

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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