Network Analysis Metrics Research Articles

Three applications of semantic network analysis to individual student think-aloud data

Student during-learning data such as think-alouds or writing are often coded for use of strategies or moves, but less often for what knowledge the student is using. However, analyzing the content of such products could yield much valuable information. A promising technique for analyzing the content of student products is semantic network analysis, more widely used in political science, communication, information science, and some other social science disciplines. We reviewed the small literature on semantic network analysis (SemNA) of individuals with relevant outcomes to identify which network analysis metrics might be suitable. The Knowledge Integration (KI) framework from science education is discussed as focusing on amount and structure of student knowledge, and therefore especially relevant for testing with SemNA metrics. We then re-analyze three published think-aloud data sets from undergraduate students learning introductory biology with the metrics found in the literature review. Significant relations with posttest comprehension score are found for number of nodes and edges; degree and betweenness centrality; diameter, and mean distance. Inconsistent results possibly due to text-specific features were found for number of clusters, LCC, and density, and null results were found for PageRank centrality and centralization degree. Basic principles from the KI framework are supported—amount of information (nodes), connections (edges, average degree), key ideas (degree and betweenness centrality) and length of causal chains (mean distance and diameter) are related to posttest comprehension, but not density or LCC. Possible explanations for slight variations across data sets are discussed, and alternative theories and metrics are offered.

Seed exchange networks: metrics for examining the resilience of social-ecological agricultural systems

ABSTRACT Agricultural systems are dynamic social-ecological systems which are rarely examined under relational approaches. The analysis of seed exchange networks represents a growing empirical pathway to relational thinking for examining the resilience of social-ecological agricultural systems and their dynamic cycles. We conducted a literature review to (i) explore the advances of seed exchange network research, and (ii) identify and synthesize the network metrics used for examining social-ecological structures linking human and other-than-human actors to understand the resilience of agricultural systems. Complementarily, we explored what a relational approach to seed exchange network analysis might entail. Our work shows a growing use of network analysis to explore coupled social-ecological relations in agricultural systems. However, in the past quarter century, most seed exchange case studies often omitted the inherent relational nature of network data and lacked recognition of the agency of other-than-human entities in seed exchange networks. We identified over 20 network metrics broadly used in network analysis, including structural and locational metrics such as ‘density’, ‘modularity’, and ‘centrality’. These metrics have the potential to inform about the dynamics that may either enhance or constrain the resilience of agricultural systems. For example, ‘density’ and ‘centrality’ can reveal pathways of agrobiodiversity access and key actors, respectively. This information may enhance the efficiency of agrobiodiversity flows in agricultural systems. Finally, we discuss some practical implications of adopting a relational approach to seed exchange network analysis to better understand people-agrobiodiversity relations under local, regional, and global changes.

A trophic modelling framework: Key metrics for the ecological assessment of artificial structures

As the global population expands, marine coastal ecosystems face mounting pressures from human activities, that have led to habitat deterioration and dwindling fishery resources. In this context, Artificial Reefs (ARs) have emerged as one of the promising solutions. They are generally implemented to provide habitat, to create a protective, physical boundary, to support sustainable fisheries and to facilitate ecosystem rehabilitation. Evaluating their ecological performance is crucial to ensuring they meet their objectives. Initially, assessment relied on comparing ARs to natural reefs using mainly ecological metrics which focused on fish assemblage and dynamics. Despite there being more research and documentation on effectiveness today, assessing ARs remains challenging due to the number of environmental factors that can affect the ecological systems. Moreover, ecological studies mainly used metrics that investigated the reef fish populations or ecological metrics such as fish assemblages or trophic structure that are often overlooked in studies that primarily focus on commercial fishery dynamics. Therefore, new ways of assessing artificial reef performance and the set-up of comprehensive metrics which integrate this level of complexity are needed. In this study, we focused on the "Rade de Cherbourg" in the English Channel, employing a trophic modeling approach using Ecopath with Ecosim (EwE). The study emphasizes the importance of Ecological Network Analysis (ENA) metrics for evaluating changes in the systems’ properties—such as complexity, flow diversity, and recycling capacity— which result from AR implementation. Furthermore, we identified which metrics are suitable for assessing specific AR objectives. The proposed metrics serve as a command-and-control tool for AR site managers, enabling them to evaluate the performance of each AR objective effectively. With the anticipated increase in AR projects, especially those which compensate for human impact like the Cherbourg ARs, this research offers valuable insights and future perspectives to continuously improve the ecological performance of ARs.

Restoration seed and plant material supply chains are complex social networks

Previous studies have advanced the conceptual framing of a “supply chain” to describe and examine key steps, inputs, and flows related to the production and use of seed and plant material in ecological restoration. However, these studies have paid less attention to investigating interrelationships among social actors throughout the “supply chain” stages for ecological restoration and allied activities. We applied social network analysis metrics to evaluate the number, kind, and strength of relationships among 157 social actors in the nine‐state region of the Northeastern United States, where rapid growth in restoration activities is driving a demand surge for native seed and plant materials. Restoration supply chains are more complex than previous conceptualization suggests, and we argue that the term “restoration supply networks” is more illustrative of this complexity. Results highlighted interdependency among different sectors, marked variation in input and output flows across spatial–temporal scales, and interplay among linear and nonlinear supply chain steps. The restoration supply network in the study area had a core–periphery structure, with a core comprising end‐users such as government agencies, design and build contractors, and non‐profit organizations surrounded by a periphery of seed and plant material suppliers. It is important to avoid oversimplifying seed and plant material supply chains as linear systems with strong linkages. Understanding relationship patterns among supply network actors can enhance communication, coordination, and collaboration among actors in all key restoration supply chain steps, a prerequisite to overcoming seed and plant material bottlenecks and meeting global restoration goals in the coming decades.

Research Landscape of Filipino Mathematicians: a Co-authorship Network Analysis

Research collaboration presents solutions to various problems that independent researchers might encounter; however, establishing collaboration also poses challenges for most of these researchers. In this study, we created a co-authorship network map of Filipino mathematicians using Elsevier's Scopus database and identified the top five institutions with the largest number of Filipino mathematicians, each with at least one publication in Scopus. We found that among the top three institutions, collaborations rarely occur, with less than 1% distinct inter-institutional collaborations. Various network analysis metrics were performed on the map to establish a baseline for future co-authorship network research. Mathematicians were ranked using both the PageRank algorithm and betweenness centrality to highlight important and bridging figures, respectively. We found that while Canoy SR of Mindanao State University-Iligan Institute of Technology (k = 61), Corcino RB of Cebu Normal University (k = 39), and Mendoza ER of De La Salle University (k = 27) have notable contributions, the University of the Philippines Diliman (UPD) stands out with the most mathematicians having k ≥ 10, where k is the measure of degree centrality. The Leiden community detection algorithm was employed to cluster the network map, revealing 183 distinct clusters – indicating the number of cliques, which are small, exclusive research groups. This highlights the need to strengthen collaborative efforts among Philippine institutions. Interestingly, we found that UPD encompasses a broad range of mathematics research, indicating the potential to serve as a bridge connecting different subject areas. By illustrating the research landscape, this study sheds light on Filipino mathematicians' research, contributing to the enhancement of mathematics research productivity and collaboration in the country.

Investigating the use of network analysis metrics to benchmark Industrial Symbiosis development

Circular economy (CE) aims to reduce natural resource consumption, waste generation, and related detrimental environmental, social, and economic impacts by keeping valuable materials circulating in the economy. Industrial Symbiosis (IS) contributes to CE through byproduct-exchange partnerships between firms, reducing raw material use, emissions, and waste generation, while promoting the economic growth of partner firms. However, most IS projects fail in the early stages and do not reap the full sustainability benefits of IS. A significant reason for the failure of early-stage IS projects is the lack of knowledge regarding the strategic organization of IS networks. Quantifying IS network organization and operational characteristics and benchmarking IS development can address this challenge, and enable knowledge transfer regarding IS network organization from mature IS networks to guide the advancement of new IS projects. This investigation tests multiple Ecological Network Analysis (ENA) and Social Network Analysis (SNA) metrics using three real-world IS case studies, as potential IS benchmarking tools. The results of the investigation show that ENA and SNA metrics can be used to compare IS networks and identify differences in the following characteristics: Partnership prevalence, producer–consumer relationships, linkage distribution, resource utilization, effect of indirect firm relationships, network cohesion, and network centralization. The values of the multiple network analysis metrics are found to be aligned with the development time of the IS networks and the qualitative markers of IS development stages as identified in the literature. For example, the analysis of producer–consumer relationships indicated that more than 50% of the consumer firms in the early stage IS networks had only one supplier partnership compared to less than 30% of the firms in the most mature IS network. Similarly, the analysis of network cohesion showed that the early-stage IS networks had (on average) five different isolated groups of firms compared to one cohesive group of firms in the more mature IS networks. This investigation demonstrates that network analysis of IS projects only requires anonymized and non-proprietary data, and identifies data-collection and modeling standards needed to develop an IS benchmarking framework using network analysis. The promising results of this investigation motivate future work into these standardization needs and the development of IS benchmarks using network analysis.

Utilizing network analysis to explore student qualitative inferential reasoning chains

Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from . We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks. Published by the American Physical Society 2024

Construction and Analysis of Collaborative Educational Networks based on Student Concept Maps

Network Analysis has traditionally been applied to analyzing interactions among learners in online learning platforms such as discussion boards. However, there are opportunities to bring Network Analysis to bear on networks representing learners' mental models of course material, rather than learner interactions. This paper describes the construction and analysis of collaborative educational networks based on concept maps created by undergraduates. Concept mapping activities were deployed throughout two separate quarters of a large General Education (GE) course about sustainability and technology at a large university on the West Coast of the United States. A variety of Network Analysis metrics are evaluated on their ability to predict an individual learner's understanding based on that learner's contributions to a network representing the collective understanding of all learners in the course. Several of the metrics significantly correlated with learner performance, especially those that compare an individual learner's conformity to the larger group's consensus. The novel network metrics based on collective networks of learner concept maps are shown to produce stronger and more reproducible correlations with learner performance than metrics traditionally used in the literature to evaluate concept maps. This paper thus demonstrates that Network Analysis in conjunction with collective networks of concept maps can provide insights into learners' conceptual understanding of course material.

Psychometric network analysis of the Patient Health Questionnaire-4 (PHQ-4) in Paraguayan general population.

Depression and anxiety are two of the most prevalent and disabling mental disorders worldwide, both in the general population and in outpatient clinical settings. This study aimed to analyze the psychometric properties of the Patient Health Questionnaire-4 (PHQ-4) based on network analysis metrics. A total of 911 Paraguayans (23.71% women and 76.29% men; mean age 31.25years, SD = 10.63), selected by non-probabilistic convenience sampling, participated in the study. Network analysis was used to evaluate the internal structure, reliability, and measurement invariance between men and women. The results revealed that the PHQ-4 is a unidimensional measure through Exploratory Graph Analysis (EGA). Reliability, through structural consistency, identified that 100% of the time, only a single dimension was obtained, and all items remained stable, as they were always replicated within the empirical dimension. The unidimensional structure has shown evidence of configural invariance; therefore, the network structure functioned equally among the different sex groups. The PHQ-4 presented optimal preliminary evidence of validity based on its internal structure, reliability, and invariance between sexes. Therefore, it may be useful as an accurate and brief measure of anxiety and depressive symptoms in the Paraguayan context.

Longitudinal analysis of interorganizational collaborative networks of cross-laminated timber (CLT) construction

Purpose Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction. Design/methodology/approach The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies. Findings The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors). Originality/value The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.

Network Analysis Reveals Species-Specific Organization of Microbial Communities in Four Co-Occurring Elasmobranch Species along the Georgia Coast

Comparing co-occurring species may provide insights into how aspects of ecology may play a role in influencing their microbial communities. During the 2019 commercial shrimp trawl season off coastal Georgia, swabs of skin, gills, cloaca, and gut were taken for three species of batoids (Butterfly Ray, Bluntnose Stingray, and Atlantic Stingray) and one shark species (Atlantic Sharpnose) for high-throughput sequencing of the V4 region of the bacterial 16S rRNA gene. White muscle was analyzed for stable isotopes (δ13C and δ15N) to evaluate potential niche overlap in these four sympatric mesopredators. Significant differences were found in both δ13C and δ15N signatures across species, suggesting a degree of resource partitioning. When examined within tissue type, the host species had a weak effect on β-diversity for cloaca and skin, with no differences found for gill and gut samples. However, network analysis metrics demonstrated a stronger species-specific effect and distinct microbial community relationships were apparent between the shark and batoids, with the former having tighter networks for both internally- and externally-influenced tissues (gut/cloaca and skin/gills, respectively). Despite overlapping habitat use, species’ microbiomes differed in their organizational structuring that paralleled differences in stable isotope results, suggesting a mediating role of species-specific ecology on bacterial microbiomes.

A Study on the Application of Social Network Analysis Based Approach in the Optimization of Cultural Communication Paths and Efficiency

Abstract The optimization of cultural communication paths is an important part of Chinese culture’s continued dialogue with the world in the face of increasingly fierce international competition. In this paper, we use social network analysis to collect relevant data on the microblogging platform and analyze the overall network structure of “The Belt and Road Initiative” using network density, network distance, and clustering coefficients. Using social network analysis metrics, we calculate the node position of the network path of “The Belt and Road Initiative” and analyze the influence of each microblog account. We filter the cultural communication topics of “The Belt and Road Initiative”, scrutinize the participating regions, and establish the characteristics of the cooperation mode within this cultural communication. “The Belt and Road Initiative” has a network density of 0.247, 0.281, and 0.295, respectively, which indicates that the network density of these three topics is small and there is less information exchange, but the cohesive force of cultural communication is stronger. “People’s Daily,” “Central Committee of the Communist Youth League,” and “Phoenix Video” have high intermediary centrality (712.256, 700.425, and 695.253, respectively). The high intermediary centrality indicates that these nodes have a great influence on the flow of resources in the information dissemination network and can accelerate the efficiency of the “Belt and Road Initiative” cultural dissemination path.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)—mental models that graphically represent causal relationships between a system’s factors—are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure—finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect—possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Analyzing the Spread of Misinformation on Social Networks: A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.

Mapping analytical hierarchy process research to sustainable development goals: Bibliometric and social network analysis

The drive to achieve the Sustainable Development Goals (SDGs) becomes more urgent as the 2030 deadline draws near, increasing research in various sectors. Nevertheless, studies that systematically map Analytical Hierarchy Process (AHP) publications with the SDGs need to be more conspicuously lacking. Our study adds a new perspective to the field by creatively bridging this knowledge gap using the Elsevier SDG Mapping Initiative. To find research clusters, trends, and themes linked to SDGs and their connection to environmental sustainability, we thoroughly analyzed 29,897 publications from 2012 to 2022. The analysis showed that SDG 15, SDG 7, SDG 12, SDG 13, and SDG 11 were the top five SDGs, with an environmental focus among the 17 SDGs. These top SDGs had many clusters connected to them, illustrating various sustainability-related problems. The study also looked at connections between SDGs, the nations with the highest rates of productivity, the top contributors, and the journals with the highest citation counts. We discovered three separate SDG clusters using co-occurrence network analysis, each representing a different SDG. We discovered relevant SDGs using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis and centrality indicators like eigenvector and betweenness. This novel method for publishing analysis combines an AHP focus that aligns with the SDGs with social network analysis and centrality metrics. Our research advances knowledge of how the AHP technique can assess initiatives supporting the SDGs. We offer essential insights into prioritizing sustainable development measures by identifying research clusters, trends, and issues related to environmental sustainability. This study highlights the subject's most important SDGs, productive nations, helpful organizations, and significant journals.

Network analysis of innovation mentor community of practice

PurposePositive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).Design/methodology/approachThe paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.FindingsThe work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.Originality/valueBased on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.

Descriptive network analysis and the influence of timescale on centrality and cohesion metrics from a system of between-herd dairy cow movements in Ontario, Canada

Previous research has demonstrated that static monthly networks of between-herd dairy cow movements in Ontario, Canada were highly fragmented, reducing potential for large-scale outbreaks. Extrapolating results from static networks can become problematic for diseases with an incubation period that exceeds the timescale of the network. The objectives of this research were to: 1) describe the networks of dairy cow movements in Ontario, and 2) describe the changes that occur among network analysis metrics when conducted at seven different timescales. Networks of dairy cow movements were created using Lactanet Canada milk recording data collected in Ontario between 2009 and 2018. Centrality and cohesion metrics were calculated after aggregating the data at seven timescales: weekly, monthly, semi-annual, annual, biennial, quinquennial, and decennial. There were 50,598 individual cows moved between Lactanet-enrolled farms, representing approximately 75% of provincially registered dairy herds. Most movements occurred over short distances (median = 39.18 km), with fewer long-range movements (maximum = 1150.80 km). The number of arcs increased marginally relative to the number of nodes with longer network timescales. Both mean out-degree, and mean clustering coefficients increased disproportionately with increasing timescale. Conversely, mean network density decreased with increasing timescale. The largest weak and strong components at the monthly timescale were small relative to the full network (267 and 4 nodes), whereas yearly networks had much higher values (2213 and 111 nodes). Higher relative connectivity in networks with longer timescales suggests pathogens with long incubation periods and animals with subclinical infection present increased potential for wide-spread disease transmission among dairy farms in Ontario. Careful consideration of disease-specific dynamics should be made when using static networks to model disease transmission among dairy cow populations.

Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems

[Background] Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those who are contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. [Aims] In this research, we investigate the automatic labeling of open issues strategy to help the contributors to pick a task to contribute. We label the issues with APIdomains- categories of APIs parsed from the source code used to solve the issues. We plan to add social network analysis metrics gathered from the issues conversations as new predictors. By identifying the skills, we claim the contributor candidates should pick a task more suitable to their skill. [Method] We are employing mixed methods. We qualitatively analyzed interview transcripts and the survey's open-ended questions to comprehend the strategies communities use to assist in onboarding contributors and contributors used to pick up an issue. We applied quantitative studies to analyze the relevance of the API-domain labels in a user experiment and compare the strategies' relative importance for diverse contributor roles. We also mined project and issue data from OSS repositories to build the ground truth and predictors able to infer the API-domain labels with comparable precision, recall, and F-measure with the state-of-art. We also plan to use a skill ontology to assist the matching process between contributors and tasks. By quantitatively analyzing the confidence level of the matching instances in ontologies describing contributors' skills and tasks, we might recommend issues for contribution. In addition, we will measure the effectiveness of the API-domain labels by evaluating the issues solving time and the rate among the labeled and unlabelled ones. [Results] So far, the results showed that organizing the issues?which includes assigning labels is seen as an essential strategy for diverse roles in OSS communities. The API-domain labels are relevant, mainly for experienced practitioners. The predicted labels have an average precision of 75.5%. [Conclusions] Labeling the issues with the API-domain labels indicates the skills involved in an issue. The labels represent possible libraries (aggregated into domains) used in the source code related to an issue. By investigating this research topic, we expect to assist the new contributors in finding a task, helping OSS communities to attract and retain more contributors.

Media moments: how media events and business incentives drive twitter engagement within the small business community.

Twitter is one of the most popular social networking platforms today with nearly 238 million active daily users. While the platform is used by a myriad of individuals for various purposes, businesses both large and small have begun to adopt Twitter into their business strategy to better connect with consumers. Considering the growing emphasis on social media engagement in the business sector, the present study examines some of the fastest-growing American small businesses from the perspective of media events theory. According to media events theory, certain large-scale events will attract excess viewership and attention from the public, both on traditional and digital platforms. We examine how small businesses leveraged media events of 2020, including COVID-19 and the 2020 US presidential election, so as to increase engagement and foster the growth of their businesses via Twitter. Using 35000 tweets based on media event-related hashtags collected throughout 2020, we investigated Twitter engagement among 100 of the fastest-growing small businesses in the USA. Through the use of network analysis metrics, we illustrate that businesses that tweeted about media events often received greater levels of user engagement and exerted greater influence over their respective networks.

The Who in VR/AR for Education: A Scoping Review from IEEE Publications

HEIs have continuously been the vanguard of novel technologies, pushing progress and establishing the afterward generation of scientists, businesspersons, and engineers. Virtual and augmented reality technologies are on the borderline of enlargement nowadays. To better understand who are essential actors in this new research area, we have extracted metadata of all published papers by IEEE, dating from 1990 to 2021, using keywords VR, AR, and Education. For each publication, we have extracted the list of all the authors, creating a co-authorship dataset. This dataset is then further processed using social network analysis metrics. The results are discussed to understand research publications' main actors driving VR/AR/Education trends.