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The purpose of this study is to apply altmetrics and bibliometric indicators on the top 100 most mentioned articles published related to the sustainable development goal (SDG)-13, Climate Action.

The authors used the Altmetric Explorer’s SDGs filter to extract the most mentioned articles belonging to Climate Action and their other characteristics, such as DOI, titles, tools mentioning them and their demographic descriptions. The same set of papers was searched in the Dimensions database to extract them in the format importable in R-studio to check the distribution of papers across various journals and identify their subject category, countries and institutions publishing these papers. Further, SPSS was used to check the correlation between altmetric attention score (AAS) and citations.

The results of the paper showed the mean of AAS and the citations received by the articles was 3,556.35 and 304.04, respectively. Twitter has been the most used social media platform for mentioning the research related to climate action, covering 88.1% of the total mentions. The Twitter and the News mention demographics show the USA contributing the most tweet mentions (15.2%) as well as news mentions (57.65%) to the papers. Also, the USA has solely published 49 papers from the total papers selected for the study. The papers were published in 31 journals most of them belonging to the quartile first (Q1) category and primarily belonged to the subject category “Earth Sciences.” Pearson’s correlational method showed a significant but low positive correlation between AAS and citation counts (r = 0.365, p = <0.001) and a strong positive correlation between the citations and Mendeley readership counts (r = 0.907).

The research is original in nature and discovered very interesting results about climate action using altmetric and bibliometric techniques.

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.

The study showcases the social impact (online attention) of funded research compared to nonfunded for the BRICS nations. The key themes achieving online attention across the funded and nonfunded publications have also been identified.

A total of 1,507,931 articles published across the BRICS nations for a period of three (03) years were downloaded from the Clarivate Analytics' InCites database of Web of Science (WoS). “Funding Acknowledgement Analysis (FAA)” was used to identify the funded and nonfunded publications. The altmetric score of the top highly cited (1%) publications was gauged from the largest altmetric data provider, “Altmetric.com”, using the DOI of each publication. One-way ANOVA test was used to know the impact of funding on the mentions (altmetrics) across different data sources covered by Altmetric.com. The highly predominant keywords (hotspots) have been mapped using bibliometric software, “VOSviewer”.

The mentions across all the altmetric sources for funded research are higher compared to nonfunded research for all nations. It indicates the altmetric advantage for funded research, as funded publications are more discussed, tweeted, shared and have more readers and citations; thus, acquiring more social impact/online attention compared to nonfunded publications. The difference in means for funded and nonfunded publications varies across various altmetric sources and nations. Further, the authors’ keyword analysis reveals the prominence of the respective nation names in publications of the BRICS.

The study showcases the utility of indexing the funding information and whether research funding increases social impact return (online attention). It presents altmetrics as an important impact assessment and evaluation framework indicator, adding one more dimension to the research performance. The linking of funding information with the altmetric score can be used to assess the online attention and multi-flavoured impact of a particular funding programme and source/agency of a nation so that necessary strategies would be framed to improve the reach and impact of funded research. It identifies countries that achieve significant online attention for their funded publications compared to nonfunded ones, along with the key themes that can be utilised to frame research and investment plans.

The study represents the social impact of funded research compared to nonfunded across the BRICS nations.

University students are a unique population with great potential to adopt eating habits that promote positive human and planetary health outcomes. The purpose of this study is to illustrate the current perceptions of sustainable eating behaviours among the students and to examine the determinants of sustainable eating behaviours.

Data were collected from December 2020 to May 2021 through focus group discussions among university students in Ontario, facilitated through synchronous online sessions. There were 21 student participants during the course of five focus group sessions (4–5 participants per session) from various departments within the university. The discussions were transcribed and analyzed for main themes and concepts using open coding; deductive coding based on the framework by Deliens et al. as well as the literature; and inductive coding for emerging themes.

The students had different perceptions about what constituted sustainable eating behaviours, some of which were not based on fact. A variety of individual, environmental (macro, micro and social) and university characteristics were mentioned as factors influencing sustainable food choices, with “food literacy” and “campus food” being the top two factors.

This study presents a novel and holistic overview of how sustainable eating behaviours and sustainable foods are perceived among university students and identifies the perceived determinants of adopting sustainable eating behaviours. This study helps with identifying opportunities to promote sustainable eating behaviours among university students and the design/implementation of informed interventions and policies aimed at improving eating behaviours.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

CPTED’s premise to the improvement of quality of life (QOL) is crime prevention and safety, and yet there is little concern for the impact of CPTED implementation to QOL when the crime increases after the interventions.

This study systematically analyzed articles both quantitatively and qualitatively.

This study found that the CPTED–QOL relationship discussion was highly inadequate in research. Improvement of QOL has been elevated to an unquestionable and certain truth of CPTED and yet the evidence on this is highly inconclusive.

This study is a contribution to the CPTED–QOL discussion that has been lacking.

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.