Knowledge categorization affects popularity and quality of Wikipedia articles

Not on the right rung for me? How status inconsistency leads to avoidance of status-threatening ties in NCAA

This study examines the impact of status inconsistency on status-threatening activities within NCAA Division I men's basketball teams. Specifically, we focus on a nested form of status that includes both individual and group-level elements. We argue that organizations dealing with status inconsistency stemming from such nested form face challenges in reducing status inconsistency. To maintain their deserved status, these status-inconsistent organizations tend to avoid activities that could further threaten their status, despite potential economic gains. An analysis of NCAA Division I men's basketball scheduling data from 2000 to 2019 provides robust support to our theoretical arguments. Our findings suggest that the status inconsistency between a team's status and its conference status diminished the likelihood of scheduling games with non-Division I teams, a behavior considered counter-normative in this context. This effect is most prominent among teams in "Mid Major" conferences, while teams with recent participation in the NCAA Tournament show a mitigated effect.

Modeling Popularity and Reliability of Sources in Multilingual Wikipedia

One of the most important factors impacting quality of content in Wikipedia is presence of reliable sources. By following references, readers can verify facts or find more details about described topic. A Wikipedia article can be edited independently in any of over 300 languages, even by anonymous users, therefore information about the same topic may be inconsistent. This also applies to use of references in different language versions of a particular article, so the same statement can have different sources. In this paper we analyzed over 40 million articles from the 55 most developed language versions of Wikipedia to extract information about over 200 million references and find the most popular and reliable sources. We presented 10 models for the assessment of the popularity and reliability of the sources based on analysis of meta information about the references in Wikipedia articles, page views and authors of the articles. Using DBpedia and Wikidata we automatically identified the alignment of the sources to a specific domain. Additionally, we analyzed the changes of popularity and reliability in time and identified growth leaders in each of the considered months. The results can be used for quality improvements of the content in different languages versions of Wikipedia.

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

On Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, the quality of information about the same topic depends on the language. Any interested user can improve an article and that improvement may depend on the 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 selected topics are among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we selected 27 main multilingual categories and analyzed all their connections with sub-categories based on information extracted from over 10 million categories in 55 language versions. To classify the articles to one of the 27 main categories, we took into account over 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.