Nagineedu - Indian (Telugu) actor. Article “Nagineedu” in English Wikipedia has 25.1 points for quality (as of July 1, 2025). The article contains 8 references and 7 sections.
Since the creation of article “Nagineedu”, its content was written by 75 registered users of English Wikipedia and edited by 75 registered Wikipedia users in all languages.
The article is cited 127 times in English Wikipedia and cited 127 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 | English: #50291 12.2019 |
Global: #100112 12.2019 |
| Best Rank | English: #55132 10.2017 |
Global: #217073 10.2017 |
On 10 March 2026 in multilingual Wikipedia, Internet users most often read articles on the following topics: Iran, Mojtaba Khamenei, Enhypen, Ali Khamenei, Strait of Hormuz, Jeffrey Epstein, 2026 Iran war, 2026 World Baseball Classic, Donald Trump, World Baseball Classic.
In English Wikipedia the most popular articles on that day were: 2026 Iran war, Mojtaba Khamenei, Deaths in 2026, Ali Khamenei, War Machine (upcoming film), Enhypen, Jennifer Runyon, Pete Hegseth, 2026 World Baseball Classic, Iran.
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
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(18.47+6.5+14.29+50+36.19)$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
