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This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

This paper aimed to explore the presence of altmetric data across the sub-categories of the medical science discipline and also explore whether the openness of articles results in (dis)advantage for altmetrics mentions.

The research implies data carpentry methods for gathering bibliographic data related to narrow fields of medical science discipline from the Scopus database with at least one Indian author affiliation during 2012–2021. The corresponding data were also collected from three different sources: Altmetric.com, Mendeley.com and Unpaywall.org, using OpenRefine and REST/API calls. Further, the authors observed open access altmetric advantages (OAAA) and categorical OAAA (COAAA) across seven altmetric platforms for all articles as well as discipline-wise.

The result shows that the overall coverage of altmetric events is still low, but it shows an increasing trend from the past. Mendeley has the largest coverage; almost 97.12% of publications are covered. The health policy discipline has extensive coverage across altmetric platforms (nearly 57.40% of publications in altmetrics and 99.23% in Mendeley), whereas the drug guides has the lowest (almost 0.92% in Altmetrics and 77.05% in Mendeley). Moreover, the OA articles have been highly covered in altmetrics than those of non-OA articles, and bronze OA articles covered mostly compared to others. News registered with the significant OA altmetric advantages across disciplines. Categorically, bronze and hybrid OA have the largest altmetric advantages.

This research is a unique attempt to apply OAAA and COAAA to explore OA altmetric advantages of narrow subject categories of medical science disciplines.

The first purpose of the present study is to investigate the coverage of journal articles in Physics in various sources of altmetrics. Secondly, the study investigates the relationship between altmetrics and citations. Finally, the study also investigates whether the relationship between citations and altmetrics was stronger or weaker for those articles that had been mentioned at least once in the sources of altmetrics.

The journal articles in Physics having at least one author from an Indian Institution and published during 2014–2018 in sources of altmetrics have been investigated. Altmetric.com was used for collecting altmetrics data. Spearman’s rank correlation coefficient (ρ) has been used as the data found to be skewed.

The highest coverage was found on Twitter (22.68%), followed by Facebook (3.62%) and blogs (2.18%). The coverage in the rest of the sources was less than 1%. The average Twitter mentions for journal articles tweeted at least once was found to be 4 (3.99) and for Facebook mentions, it was found to be 1.48. Correlations between Twitter mentions–citations and Facebook mentions–citation were found to be statistically significant but low to weak positive.

The study concludes that due to the low coverage of journal articles, altmetrics should be used cautiously for research evaluation keeping in mind the disciplinary differences. The study also suggests that altmetrics can function as complementary to citation-based metrics.

The study is one of the first large scale altmetrics studies dealing with research in Physics. Also, Indian research has not been attended to in the altmetrics literature and the present study shall fill that void.

The main purpose of this study is to explore and validate the question “whether altmetric mentions can predict citations to scholarly articles”. The paper attempts to explore the nature and degree of correlation between altmetrics (from ResearchGate and three social media platforms) and citations.

A large size data sample of scholarly articles published from India for the year 2016 is obtained from the Web of Science database and the corresponding altmetric data are obtained from ResearchGate and three social media platforms (Twitter, Facebook and blog through Altmetric.com aggregator). Correlations are computed between early altmetric mentions and later citation counts, for data grouped in different disciplinary groups.

Results show that the correlation between altmetric mentions and citation counts are positive, but weak. Correlations are relatively higher in the case of data from ResearchGate as compared to the data from the three social media platforms. Further, significant disciplinary differences are observed in the degree of correlations between altmetrics and citations.

The results support the idea that altmetrics do not necessarily reflect the same kind of impact as citations. However, articles that get higher altmetric attention early may actually have a slight citation advantage. Further, altmetrics from academic social networks like ResearchGate are more correlated with citations, as compared to social media platforms.

The paper has novelty in two respects. First, it takes altmetric data for a window of about 1–1.5 years after the article publication and citation counts for a longer citation window of about 3–4 years after the publication of article. Second, it is one of the first studies to analyze data from the ResearchGate platform, a popular academic social network, to understand the type and degree of correlations.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2019-0364

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.

Academic citation and social attention measure different dimensions in the impact of research results. The authors quantify the contribution of funding to both indicators considering the differences attributable to the research field and access type.

Citation and social attention accumulated until the year 2021 of more than 367 thousand research articles published in the year 2018 are studied. The authors consider funding acknowledgments (FAs) in the research articles. The data source is Dimensions, and the units of study are research articles in the United Nation (UN) Sustainable Development Goals (SDGs).

Most cited goals by researchers do not coincide with those that arouse greater social attention. A small proportion of articles accumulates a large part of the citations and most of the social attention. Both citation and social attention grow with funding. Thus, funded research has a greater probability of being cited in academic articles and mentioned in social media. Funded research receives on average two to three times more citations and 2.5 to 4.5 times more social attention than unfunded research. Moreover, the open access (OA) modalities gold and hybrid have the greatest advantages in citation and social attention due to funding.

Specific topics were studied in a specific period. Studying other topics and/or different time periods might result in different findings.

When funding to publish in open or hybrid access journals is not available, it is advisable to self-archiving the pre-print or post-print version in a freely accessible repository.

Although cautiously, it is also advisable to consider the social impact of the research to complement the scientific impact in the evaluation of the research.

The joint evaluation of the effect of both funding and OA on social attention.

The purpose of this paper is to spotlight the research output in the field of grey literature (GL) during the 13-year period between 2007 and 2019 as reflected in the Scopus database.

This study has used Scopus, an abstract and citation database of peer-reviewed literature, to collect data for the specified 13-year period. The sources, which had “grey literature” in their titles, were considered for the study and the period was from 2007 to 2019. The downloaded results were analysed using specific parameters. The SPSS and Excel have been used for analysing the retrieved data. The VOSviewer has been used for visualizing the network.

Scopus indexes different kinds of documents such as articles, books, chapters, conference papers, editorial, erratum, letters, notes, reviews and short surveys. There were 345 publications, which received 309 citations. GL-Conference Series: Conference Proceedings published the most number of articles. The majority 51.9 per cent of the articles were published in collaboration with authors from European countries. The term “grey literature” occurred most commonly as author keyword and index term.

The present study highlights how the area of GL has evolved during the 13-year period. The findings of the study pave the way for more detailed exploration of GL, which has relevance for the researchers.

Numerous research tools exist but their usage among researchers across the different phases of the research cycle of scholarly communication remains unknown. This research aims to address this knowledge gap by mapping the research tools frequently used by global researchers against the various phases of the research cycle of scholarly communication.

This research adopts a descriptive research design and conducts a cross-tabulation of secondary data consisting of 20,663 useable responses in a global survey of research tools for scholarly communication. This research also administered a survey to academic experts to classify the research tools according to traditional, modern, innovative and experimental categories.

This research reveals the six phases of the research cycle (i.e. discovery, analysis, writing, publication, outreach and assessment) and the research tools of scholarly communication frequently used by researchers worldwide in each phase as a whole and by roles, disciplines, regions and career stages. Notably, this research indicates that most of the research tools used by researchers are classified as “modern” and “innovative”.

The original insights herein should be useful for both established and early career researchers to gain and share research insights, as well as policymakers and existing and aspiring service providers who wish to improve the utility and usage of research tools for scholarly communication. Notably, this research represents a seminal endeavor at enhancing a global survey (secondary research) using a follow-up expert survey (primary research), which enabled the organization of research tools for scholarly communication into four refined categories. In doing so, this research contributes finer-grained insights that showcase the importance of keeping up with the advancement of technology through the use of modern, innovative and experimental research tools, thereby highlighting the need to go beyond traditional research tools for scholarly communication.

Over the past few years, assemblage theory or assemblage thinking has garnered increasing attention in educational research, but has been used only tangentially in explications of the nature of comparative and international education (CIE) as a field. This conceptual examination applies an assemblage theory lens to explore the contours of CIE as a scholarly field marked by its rich and interweaved architecture. It does so by first reviewing Deleuze and Guattari’s (1987) principles of rhizomatic structures to define the emergence of assemblages. Secondly, it transposes these principles in conceiving the field of CIE as a meta-assemblage of associated and subordinated sub-assemblages of actors driven by varied disciplinary, interdisciplinary or multidisciplinary interests. Finally, it interrogates the role of Big Data technologies in exerting (re)territorializing and deterritorializing tendencies on the (re)configuration of CIE. The chapter concludes with reiterating the variable character of CIE as a meta-assemblage and proposes ways to move this conversation forward.