Abstract This study explored the spatiotemporal relationship between usage data (measured by PDF downloads and HTML views) and topic popularity (measured by the number of publications) in scientific literature. Using a panel dataset of over 2.3 million papers and 130 million usage records from IEEE Xplore, we develop a theoretical framework grounded in attention economy theory and the competitive exclusion principle. By using fixed effects model, the instrumental variable method, and the spatial Durbin model, we discover that how often a topic is used greatly increases its future popularity, while usage data from related topics have a negative impact. This study provides solid preliminary evidence for using usage data in detecting research hotspots. Additionally, this study innovatively proposes two methods for constructing spatial weight matrices based on topic semantic vectors, offering a concrete pathway for integrating spatial econometrics with spatial scientometrics.

Abstract The phenomenon of co‐corresponding authorship is becoming more and more common. To understand the practice of authorship credit sharing among multiple corresponding authors, we comprehensively analyzed the characteristics of the phenomenon of co‐corresponding authorships from the perspectives of countries, disciplines, journals, and articles. This researcher was based on a dataset of nearly 8 million articles indexed in the Web of Science, which provides systematic, cross‐disciplinary, and large‐scale evidence for understanding the phenomenon of co‐corresponding authorship for the first time. Our findings reveal that higher proportions of co‐corresponding authorship exist in Asian countries, especially in China. From the perspective of disciplines, there is a relatively higher proportion of co‐corresponding authorship in the fields of engineering and medicine, while a lower proportion exists in the humanities, social sciences, and computer science fields. From the perspective of journals, high‐quality journals usually have higher proportions of co‐corresponding authorship. At the level of the article, our findings proved that, compared to articles with a single corresponding author, articles with multiple corresponding authors have a significant citation advantage.

Abstract This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

Abstract To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.