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.

In this paper the impact of large language models (LLM) on the fake news phenomenon is analysed. On the one hand decent text‐generation capabilities can be misused for mass fake news production. On the other, LLMs trained on huge volumes of text have already accumulated information on many facts thus one may assume they could be used for fact‐checking. Experiments were designed and conducted to verify how much LLM responses are aligned with actual fact‐checking verdicts. The research methodology consists of an experimental dataset preparation and a protocol for interacting with ChatGPT, currently the most sophisticated LLM. A research corpus was explicitly composed for the purpose of this work consisting of several thousand claims randomly selected from claim reviews published by fact‐checkers. Findings include: it is difficult to align the responses of ChatGPT with explanations provided by fact‐checkers; prompts have significant impact on the bias of responses. ChatGPT at the current state can be used as a support in fact‐checking but cannot verify claims directly.

Despite the fact that Wikipedia is often criticized for its poor quality, it continues to be one of the most popular knowledge bases in the world. Articles in this free encyclopedia on various topics can be created and edited in about 300 different language versions independently. Our research has showed that in language sensitive topics, the quality of information can be relatively better in the relevant language versions. However, in most cases, it is difficult for the Wikipedia readers to determine the language affiliation of the described subject. Additionally, each language edition of Wikipedia can have own rules in the manual assessing of the content’s quality. There are also differences in grading schemes between language versions: some use a 6–8 grade system to assess articles, and some are limited to 2–3. This makes automatic quality comparison of articles between various languages a challenging task, particularly if we take into account a large number of unassessed articles; some of the Wikipedia language editions have over 99% of articles without a quality grade. The paper presents the results of a relative quality and popularity assessment of over 28 million articles in 44 selected language versions. Comparative analysis of the quality and the popularity of articles in popular topics was also conducted. Additionally, the correlation between quality and popularity of Wikipedia articles of selected topics in various languages was investigated. The proposed method allows us to find articles with information of better quality that can be used to automatically enrich other language editions of 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.