nashaniva.by

Nasha Niva is one of the oldest Belarusian weekly newspapers, founded in 1906 and re-established in 1991. Naša Niva became a cultural symbol, due to the newspaper's importance as a publisher of Belarusian literature and as a pioneer of Belarusian language journalism, the years before the October Revolution are often referred to as the Naša Niva Period. More information...

Multilingual Wikipedia

In June 2020 the website nashaniva.by was on the 7,924th place in the ranking of the most reliable and popular sources in multilingual Wikipedia from readers' point of view (PR-score). If we consider only frequency of appearance of this source in references of Wikipedia articles (F-score), this website was on the 13,427th place in June 2020. From Wikipedians' point of view, "nashaniva.by" is the 15,424th most reliable source in different language versions of Wikipedia (AR-score).

The website is placed before ifremer.fr and after sukhoi.org in multilingual PR ranking of the most reliable sources in Wikipedia.

PR-score:
7,924th place
3,851,876
+3,851,876
AR-score:
15,424th place
251,466
+251,466
F-score:
13,427th place
860
+860

Russian Wikipedia (ru)

PR-score:
553rd place
3,818,861
+3,818,861
AR-score:
859th place
247,524
+247,524
F-score:
918th place
832
+832

Belarusian Wikipedia (be)

PR-score:
237th place
11,238
+11,238
AR-score:
7,479th place
251
+251
F-score:
2,249th place
10
+10

Polish Wikipedia (pl)

PR-score:
35,778th place
7,209
+7,209
AR-score:
31,987th place
1,960
+1,960
F-score:
32,058th place
7
+7

Ukrainian Wikipedia (uk)

PR-score:
12,863rd place
6,706
+6,706
AR-score:
93,630th place
100
+100
F-score:
29,303rd place
5
+5

English Wikipedia (en)

PR-score:
587,870th place
5,860
+5,860
AR-score:
528,979th place
1,380
+1,380
F-score:
976,250th place
1
+1

Latvian Wikipedia (lv)

PR-score:
3,088th place
2,000
+2,000
AR-score:
10,068th place
250
+250
F-score:
3,926th place
5
+5

BestRef shows popularity and reliability scores for sources in references of Wikipedia articles in different languages. Data extraction based on complex method using Wikimedia dumps. To find the most popular and reliable sources we used information about over 200 million references of Wikipedia articles. More details...