A Systematic Identification and Analysis of Scientists on Twitter

Metrics derived from Twitter and other social media---often referred to as altmetrics---are increasingly used to estimate the broader social impacts of scholarship. Such efforts, however, may produce highly misleading results, as the entities that participate in conversations about science on these platforms are largely unknown. For instance, if altmetric activities are generated mainly by scientists, does it really capture broader social impacts of science? Here we present a systematic approach to identifying and analyzing scientists on Twitter. Our method can identify scientists across many disciplines, without relying on external bibliographic data, and be easily adapted to identify other stakeholder groups in science. We investigate the demographics, sharing behaviors, and interconnectivity of the identified scientists. We find that Twitter has been employed by scholars across the disciplinary spectrum, with an over-representation of social and computer and information scientists; under-representation of mathematical, physical, and life scientists; and a better representation of women compared to scholarly publishing. Analysis of the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a small fraction of shared URLs are science-related. We find an assortative mixing with respect to disciplines in the networks between scientists, suggesting the maintenance of disciplinary walls in social media. Our work contributes to the literature both methodologically and conceptually---we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists, thus providing foundational information for the construction and use of indicators on the basis of social media metrics

Graph Learning and Its Applications: A Holistic Survey

Graph learning is a prevalent domain that endeavors to learn the intricate relationships among nodes and the topological structure of graphs. These relationships endow graphs with uniqueness compared to conventional tabular data, as nodes rely on non-Euclidean space and encompass rich information to exploit. Over the years, graph learning has transcended from graph theory to graph data mining. With the advent of representation learning, it has attained remarkable performance in diverse scenarios, including text, image, chemistry, and biology. Owing to its extensive application prospects, graph learning attracts copious attention from the academic community. Despite numerous works proposed to tackle different problems in graph learning, there is a demand to survey previous valuable works. While some researchers have perceived this phenomenon and accomplished impressive surveys on graph learning, they failed to connect related objectives, methods, and applications in a more coherent way. As a result, they did not encompass current ample scenarios and challenging problems due to the rapid expansion of graph learning. Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning. Specifically, we commence by proposing a taxonomy from the perspective of the composition of graph data and then summarize the methods employed in graph learning. We then provide a detailed elucidation of mainstream applications. Finally, based on the current trend of techniques, we propose future directions.Comment: 20 pages, 7 figures, 3 table

Collaborative yet independent: Information practices in the physical sciences

In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data. This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research. Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies

Ranking in evolving complex networks

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google’s PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes

Analysis in Web 3D Environments of Thematic Research Networks on Immersive Learning through Variation of Clustering Criteria

Este projeto tem como objetivo desenvolver uma ferramenta de visualização 3D baseada na web que proporciona uma compreensão global do campo de Aprendizagem Imersiva. As redes temáti- cas são uma abordagem bem estabelecida para lidar com esses desafios. Portanto, foi realizada uma revisão sistemática da literatura para extrair métodos e critérios de clustering em redes temáticas. A metodologia empregada neste estudo é a pesquisa de Design Science Research, que envolveu o desenvolvimento e a avaliação iterativos da ferramenta de visualização. Entre- vistas com especialistas foram realizadas para identificar os requisitos, e métodos rigorosos, in- cluindo gravação, análise e transcrição das entrevistas, foram aplicados para verificar a relevância da pesquisa. A ferramenta utiliza uma abordagem de visualização de node-link para visualizar estratégias, práticas e artigos associados à aprendizagem imersiva. Além disso, oferece uma var- iedade de funcionalidades de filtragem, permitindo que os usuários filtrem por estratégias, práticas, autores, instituições e outros. Além disso, a ferramenta incorpora várias funcionalidades de clus- tering, como detecção de comunidades usando o algoritmo de Louvain, com variação de critérios de clustering , como associação de temas e de artigos, citação de artigos, co-citação e outros. Os usuários também podem controlar a estrutura da rede modificando o tamanho das clustering, o número e as cores das comunidades. A ferramenta apresenta métodos exploratórios de redes temáticas para navegar no ambiente. Ao combinar redes temáticas com capacidades de clustering e filtragem, essa ferramenta tem como objetivo fornecer uma compreensão global do campo cien- tífico. Sua integração única de tecnologias Web e 3D, juntamente com métodos exploratórios, a diferencia das ferramentas de visualização existentes. Os poderosos algoritmos de clustering da ferramenta, oferecendo critérios diversos para entender as relações conceituais, têm o potencial de ter um impacto significativo na comunidade de aprendizagem imersiva. Ela é projetada para servir como um artefato inovador que aprimora as capacidades analíticas de pesquisadores, educadores e estudantes na área.This project aims to develop a Web-based 3D visualization tool that provides a global understanding of the field of Immersive Learning. Thematic networks are a well-established approach for addressing such challenges. Therefore, a systematic literature review was conducted to extract clustering methods and criteria in thematic networks. The methodology employed in this study is Design Science research, which involved iterative development and evaluation of the visualization tool. Expert interviews were conducted to identify requirements, and rigorous methods, including recording, analyzing, and transcribing interviews, were applied to ascertain the research's relevance. The tool utilizes a node-link visualization approach to represent immersive learning strategies, practices, and associated papers. Additionally, it offers a range of filtering functionali- ties, allowing users to filter by strategies, practices, authors, institutions, and more. Furthermore, the tool incorporates various clustering functionalities, such as community detection using the Louvain Algorithm, with variable clustering criteria such as theme and paper association, paper citation, co-citation, and others. Users can also control the network's structure by modifying clus- ter size, number, and community colors. The tool features thematic networks exploratory methods for navigating the environment. By combining thematic networks with clustering and filtering capabilities, this tool aims to provide a global understanding of the scientific field. Its unique integration of Web and 3D technologies, along with exploratory methods, distinguishes it from existing visualization tools. The tool's powerful clustering algorithms, offering diverse criteria for understanding concept relationships, have the potential to make a significant impact in the immersive learning community. It is designed to serve as an innovative artifact that enhances the analytical capabilities of researchers, educators, and students in the field