Article “青森県住宅供給公社巨額横領事件” in Japanese Wikipedia has 22.2 points for quality (as of July 1, 2025). The article contains 15 references and 3 sections. The article also contains templates indicating quality issues, therefore its score was reduced by 1.17 points.
Since the creation of article “青森県住宅供給公社巨額横領事件”, its content was written by 25 registered users of Japanese Wikipedia and edited by 25 registered Wikipedia users in all languages.
The article is cited 17 times in Japanese Wikipedia and cited 17 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 July 1, 2025 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #11738 10.2024 |
Global: #198040 10.2024 |
| Best Rank | Japanese: #5875 05.2019 |
Global: #324986 05.2019 |
On 12 August 2025 in multilingual Wikipedia, Internet users most often read articles on the following topics: Cristiano Ronaldo, Wednesday, Georgina Rodríguez, ChatGPT, Weapons, Jenna Ortega, deaths in 2025, Taylor Swift, Miguel Uribe Turbay, 2025–26 UEFA Champions League.
In Japanese Wikipedia the most popular articles on that day were: 日本航空123便墜落事故, 坂本九, 二階堂ふみ, カズレーザー, 北原遥子, やなせたかし, 柏木由紀子, 大竹七未, 大島花子, 舞坂ゆき子.
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 July, 2025. When calculating current popularity and AI of articles data from June 2025 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2025... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(5.65+5.42+0+9.68+95.98)-1.17$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
