Leveraging Citation Networks to Visualize Scholarly Influence Over Time

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars

Web 2.0 and destination marketing: current trends and future directions

Over the last decade, destination marketers and Destination Marketing Organizations (DMOs) have increasingly invested in Web 2.0 technologies as a cost-effective means of promoting destinations online, in the face of drastic marketing budgets cuts. Recent scholarly and industry research has emphasized that Web 2.0 plays an increasing role in destination marketing. However, no comprehensive appraisal of this research area has been conducted so far. To address this gap, this study conducts a quantitative literature review to examine the extent to which Web 2.0 features in destination marketing research that was published until December 2019, by identifying research topics, gaps and future directions, and designing a theory-driven agenda for future research. The study’s findings indicate an increase in scholarly literature revolving around the adoption and use of Web 2.0 for destination marketing purposes. However, the emerging research field is fragmented in scope and displays several gaps. Most of the studies are descriptive in nature and a strong overarching conceptual framework that might help identify critical destination marketing problems linked to Web 2.0 technologies is missing

Leveraging citation networks to generate narrative visualizations of scholars’ careers

We present an approach for generating dynamic narrative visualizations of scholars' careers. This approach includes an animated node-link diagram which shows the citation network accumulated around the researcher over the course of the career, with nodes and links appearing as the representation of the career progresses. Additional data provide more richness to the narrative, including timelines of key indicators, career milestones, and excerpts from qualitative interviews with the scholars. The intended audiences for this work include the scholars, who can enjoy and gain insight from a new way of looking back on their careers, and funding agencies, who have an interest in finding ways to evaluate the impact that their scholars have had

The State of Altmetrics: A Tenth Anniversary Celebration

Altmetric’s mission is to help others understand the influence of research online.We collate what people are saying about published research in sources such as the mainstream media, policy documents, social networks, blogs, and other scholarly and non-scholarly forums to provide a more robust picture of the influence and reach of scholarly work. Altmetric works with some of the biggest publishers, funders, businesses and institutions around the world to deliver this data in an accessible and reliable format. Contents Altmetrics, Ten Years Later, Euan Adie (Altmetric (founder) & Overton) Reflections on Altmetrics, Gemma Derrick (University of Lancaster), Fereshteh Didegah (Karolinska Institutet & Simon Fraser University), Paul Groth (University of Amsterdam), Cameron Neylon (Curtin University), Jason Priem (Our Research), Shenmeng Xu (University of North Carolina at Chapel Hill), Zohreh Zahedi (Leiden University) Worldwide Awareness and Use of Altmetrics, Yin-Leng Theng (Nanyang Technological University) Leveraging Machine Learning on Altmetrics Big Data, Saeed-Ul Hassan (Information Technology University), Naif R. Aljohani (King Abdulaziz University), Timothy D. Bowman (Wayne State University) Altmetrics as Social-Spatial Sensors, Vanash M. Patel (West Hertfordshire Hospitals NHS Trust), Robin Haunschild (Max Planck Institute for Solid State Research), Lutz Bornmann (Administrative Headquarters of the Max Planck Society) Altmetric’s Fable of the Hare and the Tortoise, Mike Taylor (Digital Science) The Future of Altmetrics: A Community Vision, Liesa Ross (Altmetric), Stacy Konkiel (Altmetric

Corporate Branding: An Interdisciplinary Literature Review

Purpose: This paper examines how scholarly research on corporate branding has evolved using bibliometric author co-citation analysis of articles published between 1969 and 2008 on corporate branding. Design/methodology/approach: The bibliography was compiled using the ISI Web of Science database. We searched articles published between 1969 and 2008 that used terms in their title related to our research scope. Then we used citation mapping to visualize the relationships between and among key works in the field. Findings: Our search resulted in 264 papers by 412 authors in 150 journals. The field is notably interdisciplinary, with articles published mainly in business, management, architecture, arts and communications disciplines. We found three main approaches to corporate branding research (internal, transactional, external) with seven core research streams: (1) product, service and sponsorship evaluation; (2) corporate and visual identity; (3) employment image and application; (4) corporate crime; (5) financial performance; (6) brand extension; and (7) corporate image. We also identified emerging fields such as corporate branding combined with corporate social responsibility. Research limitations: This research is limited by the database and the terms used for the search. Self-citations were also included. We used citation mapping and content analysis to identify core research streams. Originality/value: The article is singular in using bibliometrics by means of author co-citation analyses to identify, analyze and visualize key articles about corporate branding in the last 40 years. The results demonstrate the impact of selected institutions, journals, and key articles and authors on the research field

Social Media as a Political Platform in Africa: A Bibliometric Analysis

This study employs bibliometric analysis to scrutinize the pivotal journals, institutions, and countries at the nexus of social media and political discourse in Africa. Utilizing VOSviewer, a dataset of 123 publications from 2013 to 2023 was culled from Scopus Database. The analysis encompasses diverse methodologies, each tailored to the specific nature of bibliometric investigation. "Information, Communication and Society" emerged as the foremost journal in this domain, while South Africa spearheaded contributions, followed by the United States and the United Kingdom. The National Research Foundation played a prominent role as an influential institution. Notably, four distinct thematic clusters emerged, illuminating significant research areas such as the role of political platforms on human rights, the influence of social media on community engagement, the impact of media platforms on African conflicts, and social media's contribution to freedom through discourse. This study represents a pioneering bibliometric endeavor in comprehensively gauging the landscape of social media and politics in Africa, offering valuable insights for scholars and policymakers navigating this dynamic terrain.

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.Comment: 10 page

Eigenfactor

The Eigenfactor™ is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on ResearchAssessment (DORA)