Obsolete word - Wikimedia disambiguation page. Article "廃語" in Japanese Wikipedia has 22.4 points for quality (as of November 1, 2023). The article contains 17 references and 15 sections. The article also contains quality flaw template, which reduce quality score.
In October 2023 the article "廃語" was edited by 3 authors in Japanese Wikipedia (8845th place) and written by 3 authors in all languages.
Since the creation of article "廃語", its content was written by 140 registered users of Japanese Wikipedia and edited by 140 registered Wikipedia users in all languages.
The article is cited 241 times in Japanese Wikipedia and cited 241 times in all languages.
The highest Authors Interest rank from 2001:
The highest popularity rank from 2008:
There is 1 language version for this article in the WikiRank database (of the considered 55 Wikipedia language editions).
The quality and popularity assessment was based on Wikipédia dumps from November 1, 2023 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #22710 11.2021 |
Global: #280999 11.2021 |
On 23 April 2024 in multilingual Wikipedia, Internet users most often read articles on the following topics: YouTube, Serie A, Shōgun, Fallout, Facebook, Fallout, Saint George, Saint George Day, Inter Milan, ChatGPT.
In Japanese Wikipedia the most popular articles on that day were: 宮澤博行, 関貴昭, 出口夏希, 井俣憲治, 平岩紙, 内田理央, 若葉竜也, 嵯峨京子, きゃりーぱみゅぱみゅ, 若林豪.
The WikiRank project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 44 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from November, 2023. When calculating current popularity and AI of articles data from October 2023 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2023... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(10.68+6.27+0+48.39+59.1)-2.49$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
