Abstract Universities face increasing demands to improve their visibility, public outreach, and online presence. There is a broad consensus that scientific reputation significantly increases the attention universities receive. However, in most cases estimates of scientific reputation are based on composite or weighted indicators and absolute positions in university rankings. In this study, we adopt a more granular approach to assessment of universities' scientific performance using a multidimensional set of indicators from the Leiden Ranking and testing their individual effects on university Wikipedia page views. We distinguish between international and local attention and find a positive association between research performance and Wikipedia attention which holds for regions and linguistic areas. Additional analysis shows that productivity, scientific impact, and international collaboration have a curvilinear effect on universities' Wikipedia attention. This finding suggests that there may be other factors than scientific reputation driving the general public's interest in universities. Our study adds to a growing stream of work which views altmetrics as tools to deepen science–society interactions rather than direct measures of impact and recognition of scientific outputs.

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 Social media metrics have a genuine networked nature, reflecting the networking characteristics of the social media platform from where they are derived. This networked nature has been relatively less explored in the literature on altmetrics, although new network‐level approaches are starting to appear. A general conceptualization of the role of social media networks in science communication, and particularly of social media as a specific type of interface between science and society, is still missing. The aim of this paper is to provide a conceptual framework for appraising interactions between science and society in multiple directions, in what we call heterogeneous couplings. Heterogeneous couplings are conceptualized as the co‐occurrence of science and non‐science objects, actors, and interactions in online media environments. This conceptualization provides a common framework to study the interactions between science and non‐science actors as captured via online and social media platforms. The conceptualization of heterogeneous couplings opens wider opportunities for the development of network applications and analyses of the interactions between societal and scholarly entities in social media environments, paving the way toward more advanced forms of altmetrics, social (media) studies of science, and the conceptualization and operationalization of more advanced science‐society studies.

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.

The article presents three advanced citation‐based methods used to detect potential breakthrough articles among very highly cited articles. We approach the detection of such articles from three different perspectives in order to provide different typologies of breakthrough articles. In all three cases we use the hierarchical classification of scientific publications developed at CWTS based on direct citation relationships. We assume that such contextualized articles focus on similar research interests. We utilize the characteristics scores and scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly‐cited “followers,” articles not considered breakthroughs. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited article in a micro‐cluster is considered as a potential breakthrough article (M1); as well as two conceptually different methods, one that detects potential breakthrough articles among the 2% highest cited articles according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS 2% filter, knowledge diffusion is also considered (M2b). The advance citation‐based methods are explored and evaluated using validated publication sets linked to different Danish funding instruments including centers of excellence.

This study presents a large‐scale analysis of the distribution and presence of Mendeley readership scores over time and across disciplines. We study whether Mendeley readership scores (RS) can identify highly cited publications more effectively than journal citation scores (JCS). Web of Science (WoS) publications with digital object identifiers (DOIs) published during the period 2004–2013 and across five major scientific fields were analyzed. The main result of this study shows that RS are more effective (in terms of precision/recall values) than JCS to identify highly cited publications across all fields of science and publication years. The findings also show that 86.5% of all the publications are covered by Mendeley and have at least one reader. Also, the share of publications with Mendeley RS is increasing from 84% in 2004 to 89% in 2009, and decreasing from 88% in 2010 to 82% in 2013. However, it is noted that publications from 2010 onwards exhibit on average a higher density of readership versus citation scores. This indicates that compared to citation scores, RS are more prevalent for recent publications and hence they could work as an early indicator of research impact. These findings highlight the potential and value of Mendeley as a tool for scientometric purposes and particularly as a relevant tool to identify highly cited publications.

An extensive analysis of the presence of different altmetric indicators provided by Altmetric.com across scientific fields is presented, particularly focusing on their relationship with citations. Our results confirm that the presence and density of social media altmetric counts are still very low and not very frequent among scientific publications, with 15%–24% of the publications presenting some altmetric activity and concentrated on the most recent publications, although their presence is increasing over time. Publications from the social sciences, humanities, and the medical and life sciences show the highest presence of altmetrics, indicating their potential value and interest for these fields. The analysis of the relationships between altmetrics and citations confirms previous claims of positive correlations but is relatively weak, thus supporting the idea that altmetrics do not reflect the same kind of impact as citations. Also, altmetric counts do not always present a better filtering of highly‐cited publications than journal citation scores. Altmetric scores (particularly mentions in blogs) are able to identify highly‐cited publications with higher levels of precision than journal citation scores ( JCS ), but they have a lower level of recall. The value of altmetrics as a complementary tool of citation analysis is highlighted, although more research is suggested to disentangle the potential meaning and value of altmetric indicators for research evaluation.

F1000 is a postpublication peer review service for biological and medical research. F1000 recommends important publications in the biomedical literature, and from this perspective F1000 could be an interesting tool for research evaluation. By linking the complete database of F1000 recommendations to the Web of Science bibliographic database, we are able to make a comprehensive comparison between F1000 recommendations and citations. We find that about 2% of the publications in the biomedical literature receive at least one F1000 recommendation. Recommended publications on average receive 1.30 recommendations, and more than 90% of the recommendations are given within half a year after a publication has appeared. There turns out to be a clear correlation between F1000 recommendations and citations. However, the correlation is relatively weak, at least weaker than the correlation between journal impact and citations. More research is needed to identify the main reasons for differences between recommendations and citations in assessing the impact of publications.

Understanding the role of acknowledgments given by researchers in their publications has been a recurrent challenge in the bibliometric field, but relatively unexplored until now. This study presents a general bibliometric analysis on the new “funding acknowledgment” (FA) information available in the Web of Science. All publications covered by the database in 2009 have been analyzed. The presence and length of the FA text, as well as the presence of “peer interactive communication” in the acknowledgments, are related to impact indicators, distribution of papers by fields, countries of the authors, and collaboration level of the papers. It is observed that publications with FAs present a higher impact as compared with publications without them. There are also differences across countries and disciplines in the share of publications with FAs and the acknowledgment of peer interactive communication. China is the country with the highest share of publications acknowledging funding, while the presence of FAs in the humanities and social sciences is very low compared to the more basic disciplines. The presence of peer interactive communication in acknowledgments can be linked to countries that have a strong scientific tradition and are incorporated in scientific networks. Peer interactive communication is also common in the fields of humanities and social sciences and can be linked to lower levels of co‐authorship. Observed patterns are explained and topics of future research are proposed.

This study presents an analysis of the use of bibliographic references by individual scientists in three different research areas. The number and type of references that scientists include in their papers are analyzed, the relationship between the number of references and different impact‐based indicators is studied from a multivariable perspective, and the referencing patterns of scientists are related to individual factors such as their age and scientific performance. Our results show inter‐area differences in the number, type, and age of references. Within each area, the number of references per document increases with journal impact factor and paper length. Top‐performance scientists use in their papers a higher number of references, which are more recent and more frequently covered by the Web of Science. Veteran researchers tend to rely more on older literature and non‐Web of Science sources. The longer reference lists of top scientists can be explained by their tendency to publish in high impact factor journals, with stricter reference and reviewing requirements. Long reference lists suggest a broader knowledge on the current literature in a field, which is important to become a top scientist. From the perspective of the “handicap principle theory,” the sustained use of a high number of references in an author's oeuvre is a costly behavior that may indicate a serious, comprehensive, and solid research capacity, but that only the best researchers can afford. Boosting papers' citations by artificially increasing the number of references does not seem a feasible strategy.

Abstract The authors set forth a general methodology for conducting bibliometric analyses at the micro level. It combines several indicators grouped into three factors or dimensions, which characterize different aspects of scientific performance. Different profiles or “classes” of scientists are described according to their research performance in each dimension. A series of results based on the findings from the application of this methodology to the study of Spanish National Research Council scientists in Spain in three thematic areas are presented. Special emphasis is made on the identification and description of top scientists from structural and bibliometric perspectives. The effects of age on the productivity and impact of the different classes of scientists are analyzed. The classificatory approach proposed herein may prove a useful tool in support of research assessment at the individual level and for exploring potential determinants of research success.

Abstract The study of the citation histories and ageing of documents are topics that have been addressed from several perspectives, especially in the analysis of documents with “delayed recognition” or “sleeping beauties.” However, there is no general methodology that can be extensively applied for different time periods or research fields. In this article, a new methodology for the general analysis of the ageing and “durability” of scientific papers is presented. This methodology classifies documents into three general types: delayed documents, which receive the main part of their citations later than normal documents; flashes in the pan, which receive citations immediately after their publication but are not cited in the long term; and normal documents, documents with a typical distribution of citations over time. These three types of durability have been analyzed considering the whole population of documents in the Web of Science with at least 5 external citations (i.e., not considering self‐citations). Several patterns related to the three types of durability have been found and the potential for further research of the developed methodology is discussed.

Abstract The representation of science as a citation density landscape and the study of scaling rules with the field‐specific citation density as a main topological property was previously analyzed at the level of research groups. Here, the focus is on the individual researcher. In this new analysis, the size dependence of several main bibliometric indicators for a large set of individual researchers is explored. Similar results as those previously observed for research groups are described for individual researchers. The total number of citations received by scientists increases in a cumulatively advantageous way as a function of size (in terms of number of publications) for researchers in three areas: Natural Resources, Biology & Biomedicine, and Materials Science. This effect is stronger for researchers in low citation density fields. Differences found among thematic areas with different citation densities are discussed.