Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis

OBJECTIVE: The purpose of this study is to guide researchers in the COVID-19 pandemic by evaluating the 100 most cited articles of COVID-19 in terms of bibliometric analysis, Altmetric scores, and dimension badges. METHODS: “COVID-19” was entered as the search term in Thomson Reuter’s Web of Science database. The 100 most cited articles (T100) were analyzed bibliometrically. Altmetric attention scores (AASs) and dimension badge scores of the articles were evaluated. RESULTS: T100 articles were published from January to September 2020. The average citation of the top 100 articles on COVID-19 was 320 ± 344.3 (143–2676). The language of all articles was English. The average Altmetric value of T100 is 3246 ± 3795 (85–16,548) and the mean dimension badge value was 670 ± 541.6 (176–4232). Epidemiological features (n = 22) and treatment (n = 21) were at the top of the main topics of T100 articles. CONCLUSION: The more citations an article is made, the more it indicates the contribution of that article to science. However, the number of citations is not always the only indicator of article quality. The existence of methods that measure the impact of the article outside the academia to measure the value of the article arises more in an issue that affects the whole world, such as the COVID-19 pandemic.