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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.

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.

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced known and unknown biases into our measurement of social movements. We know that national newspapers tend to cover larger and more contentious events and organizations. Protest events are furthermore a small part of what social movements actually do. Without other readily available options to quantify social movements, however, big-N studies have continued to focus on protest events via a few large newspapers. With advances in digitized data and computational methods, we now no longer have to rely on large newspapers or focus only on protests to quantify important aspects of social movements. In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements. We argue that the incorporation of more data and new methods to quantify information in text has the potential to transform the way we both conceive of and measure social movements in three ways: (1) the type of focal social movement organization included, (2) the type of tactics and issues covered, and (3) the ability to go beyond protest events as the primary unit of analysis. In addition to demonstrating ways that the focus on counting protest events has introduced specific biases in the type of tactics, issues, and organizations covered in social movement research, we argue that computational methods can help us extract and count meaningful aspects of social movements well beyond event counts. In short, the infusion of new data and methods into social movements, peace, and conflict studies could lead us to a substantial shift in the way we quantify social movements, from protest events to everything that occurs outside of them.

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.

The number of children using and owning mobile devices has grown significantly in the last decade. By applying a uses and gratifications approach, this paper aims to explore what customers of a tablet computer for children report about the use and expectations of these devices from an information behavior perspective.

For this study, 1,185 online customer reviews published for two different versions of the Amazon Fire Tablet Kids Edition on the German Amazon website between June 16, 2019, and June 15, 2020, were analyzed. A content analysis of the reviews was conducted using different inductive coding methods.

Findings indicate that customers describe different aspects of children's use and families' expectations of tablets within their reviews. The expressed gratifications mostly relate to the aim of entertainment. Intentional information seeking activities were hardly mentioned within the reviews, but many customers emphasize learning as an important activity with the devices. Overall, the customer reviews reveal a mix of gratifications that differ from reported motivations for adults' tablet use.

The possibility of manipulated online customer reviews must be considered. It should also be viewed critically that the children's perspectives are only indirectly included in the data.

Families' expectations of tablets as a device for children have not been a focus of research to date. This study uses an innovative research design by applying a uses and gratifications approach to online customer reviews for children's tablets. The findings add to previous research on children's use and families' expectations of tablets and contribute to our understanding of children's information behavior in connection with mobile devices.

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.