Article "警視庁サイバー犯罪対策課" in Japanese Wikipedia has 14.8 points for quality (as of August 1, 2024). The article contains 10 references and 9 sections. The article also contains quality flaw template, which reduce quality score.
Since the creation of article "警視庁サイバー犯罪対策課", its content was written by 24 registered users of Japanese Wikipedia and edited by 24 registered Wikipedia users in all languages.
The article is cited 58 times in Japanese Wikipedia and cited 58 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 August 1, 2024 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #85608 05.2018 |
Global: #692944 02.2016 |
| Best Rank | Japanese: #6048 05.2019 |
Global: #325133 05.2019 |
On 20 February 2025 in multilingual Wikipedia, Internet users most often read articles on the following topics: Pope Francis, Elon Musk, Donald Trump, Zero Day, 2024–25 UEFA Champions League, killing of Gabby Petito, Kylian Mbappé, UEFA Champions League, ChatGPT, Volodymyr Zelenskyy.
In Japanese Wikipedia the most popular articles on that day were: つば九郎, 清水美依紗, 伊藤詩織, 大江麻理子, Null, ひょうろく, 寺西拓人, 妃海風, 大森元貴, Milet.
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 August, 2024. When calculating current popularity and AI of articles data from July 2024 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2023... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(10.83+3.73+0+29.03+34.51)-0.78$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
