[3N5-GS-11] AI and Society

[3N5-GS-11-04] Analysis of citation counts and altmetrics for important preprints

〇Kosuke Maeyama¹, Kazuhiro Kazama¹, Mitsuo Yoshida², Sho Sato³, Marie Katsurai³ (1. Wakayama University, 2. University of Tsukuba, 3. Doshisha University)

Keywords:altmetrics, citation count, arXiv

In recent years, the widespread use of preprint servers such as arXiv has enabled the rapid release of academic information, which has contributed greatly to the rapid development of deep learning and COVID-19 diagnostic, therapeutic, and drug technologies. However, the quality of preprints is a mix of good and bad because they are not peer-reviewed, so a method to support the discovery of important and reliable preprints is needed. However, it is known that the number of citations used up to now lags behind by years before it increases. In this paper, we analyze the relationship between the number of citations and the number of mentions on Twitter, which is one of the altmetrics, and the time-series changes in the number of mentions of arXiv preprints with high citation counts. Furthermore, we discuss whether the number of mentions can be used for early detection of preprints that will be highly cited in the future.