Measuring social media attention of scientific research on Novel Coronavirus Disease 2019 (COVID-19): An investigation on article-level metrics data of Dimensions

Abstract

The purpose of this research was to evaluate the rate of attention to the scientific productions on COVID-19 in social media over a period of four months. The present research was an applied descriptive-analytical study that used Scientometrics analysis. The population study included research papers about the COVID-19 indexed in Dimensions platform from December 2019 to March 2020. Information of 20% of the articles with the highest citation count and 20% of the articles with the highest number of Altmetric Attention Score (AAS), including title, journal, citation and Altmetrics indicators for each article were extracted. These data were analyzed by SPSS 16.0. The results showed that 1910 scientific productions about the COVID-19 were indexed in the Dimensions platform. A considerable number of these articles were accessible via preprint services and were published in journals such as The Lancet, JAMA, BMJ and NEJM. Authors from China and Japan were the most active authors. All of the 382 articles had AAS, that is, all of these articles received attention at least in one of the social media. The highest and lowest AAS for these articles were 14030 and 6, respectively. These results show a high rate of attention by researches and users of social media to the articles presented on COVID-19. The result of this research also showed a significant positive relationship between the citations and Altmetric indicators. As the results of this research showed, the “information supply” in the field of COVID-19 was remarkably high. Also, the “information demand” by the social media audiences was high.

Causal Modeling of Twitter Activity during COVID-19

“…Themes of previous studies that focus on exploration of, description of, correlation of, or predictive modeling with Twitter data during COVID-19 pandemic include sentiment analysis [17,[25][26][27][28], public attitude/interest measurement [21,[29][30][31], content analysis [15,[32][33][34][35][36], topic modeling [16,26,27,[37][38][39][40], analysis of misinformation, disinformation, or conspiracies [20,[41][42][43][44][45][46], outbreak detection or disease nowcasting/forecasting [18,19], and more [47][48][49][50][51][52]. Similarly, data from other social media channels (e.g., Weibo, Reddit, Facebook) or search engine statistics are utilized for parallel analyses related to COVID-19 pandemic as well [53][54][55][56][57][58][59][60][61]…”

Top COVID-19 100 vaccine papers: An Altmetric study

“…For example, Mahmoudi [9] found that most of the top papers' authors about the virus are males, while women represented a small percentage of the papers. Batooli and Sayyah [10] showed that one of the characteristics of the virus research is that a significant portion of the authors is from China and Japan. The study also revealed a correlation between Altmetrics and bibliometrics, which means that Altmetrics can serve as an indicator of papers performance and citation count.…”

Causal Modeling of Twitter Activity During COVID-19

“…Themes of previous studies that focus on exploration of, description of, correlation of, or predictive modeling with Twitter data during COVID-19 pandemic include sentiment analysis [17], [25], [26], [27], [28], public attitude/interest measurement [21], [29], [30], [31], content analysis [32], [33], [15], [34], [35], [36], topic modeling [37], [16], [38], [39], [40], [26], [27], analysis of misinformation, disinformation, or conspiracies [41], [20], [42], [43], [44], [45], [46], outbreak detection or disease nowcasting/forecasting [19], [18], and more [47], [48], [49], [50], [51], [52]. Similarly, data from other social media channels (e.g.…”