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 To unravel the linguistic dynamics of science communication on social media, this study presents a large‐scale, cross‐disciplinary analysis of language use in over 21 million Twitter mentions of 6.7 million scientific publications. While English dominates—accounting for 90.8% of all mentions and serving as a bridging language for the international dissemination of research—90 non‐English languages contribute to a rich and diverse multilingual ecosystem. A strong alignment is observed between the language of non‐English publications and their corresponding Twitter mentions, particularly for languages such as Japanese and Spanish, reflecting linguistic proximity and regional engagement. Importantly, non‐English tweets achieve user engagement levels comparable to those written in English, whereas tweets lacking meaningful textual content consistently receive lower interaction. These findings highlight the inherently multilingual nature of science communication on Twitter and underscore the importance of incorporating non‐English activities into altmetric analyses to ensure a more inclusive and equitable understanding of global scientific discourse.

Abstract To provide a dynamic perspective on the evolution of Mendeley readership, this study conducts an 8‐year longitudinal analysis of approximately 3.4 million scholarly papers published in 2015. Mendeley readership data were collected annually from 2016 to 2023 for the sampled papers to analyze the temporal accumulation patterns of readership following publication. The results indicate that Mendeley readership exhibits a speed advantage compared to citations and a prevalence advantage compared to Twitter mentions, demonstrating both initial prevalence and sustained growth on a yearly basis. However, the patterns of accumulation vary across disciplines, with papers in Biomedical and Health Sciences showing the fastest accrual of extensive Mendeley readership data. Leveraging demographic data provided by Mendeley, this study further investigates how different user groups—categorized by academic status, disciplinary affiliation, and geographic location—engage with papers across various disciplines. The findings highlight Mendeley readership as a rapid and substantial altmetric, yet they also emphasize the need to interpret the nature of the attention captured by Mendeley readership with caution, considering its potential biases introduced by the varying engagement levels of different user groups across disciplines.

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 The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.

Abstract The data re‐collection for tweets from data snapshots is a common methodological step in Twitter‐based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID‐19‐related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. “Revived” tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers.

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.

Abstract This study investigated the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions received by the 1,154 most tweeted scientific publications recorded by Altmetric.com up to October 2017. The results show that of the Twitter mentions for these highly tweeted publications, about 14.3% had become unavailable by April 2019. Deletion of tweets by users is the main reason for unavailability, followed by suspension and protection of Twitter user accounts. This study proposes two measures for describing the Twitter dissemination structures of publications: Degree of Originality (i.e., the proportion of original tweets received by an article) and Degree of Concentration (i.e., the degree to which retweets concentrate on a single original tweet). Twitter metrics of publications with relatively low Degree of Originality and relatively high Degree of Concentration were observed to be at greater risk of becoming unstable due to the potential disappearance of their Twitter mentions. In light of these results, we emphasize the importance of paying attention to the potential risk of unstable Twitter counts, and the significance of identifying the different Twitter dissemination structures when studying the Twitter metrics of scientific publications.