Abstract:International audienceDefining digital humanities might be an endless debate if we stick to the discussion about the boundaries of this concept as an academic “discipline”. In an attempt to concretely identify this field and its actors, this paper shows that it is possible to analyse them through Twitter, a social media widely used by this “community of practice”. Based on a network analysis of 2,500 users identified as members of this movement, the visualisation of the “who’s following who?” graph allows us t… Show more
“…Scholarly communities on Twitter have received significant attention in the literature over the last few years. Specific fields have included science (Weller & Puschmann, 2011), education (Veletsianos, 2012;Veletsianos & Kimmons, 2016), and the digital humanities (Quan-Haase, et al, 2015;Grandjean, 2016). At the end of their study of Twitter use by education scholars, Veletsianos and Kimmons (2016) noted areas for future research, including "the comparison of traditional scholarly output measures to Twitter impact metrics [and] the analysis of role, gender, race, and age differences regarding hashtag use" (p. 9).…”
Section: Social Media and Scholarly Communicationmentioning
confidence: 99%
“…Centrality is not one thing, but a family of concepts (Borgatti, 2013). We specifically utilized degree centrality in determining the most prominent members of #critlib, following established conventions in similar studies (Grandjean, 2016;Riddell, et al, 2017;Tremayne, 2014). Degree centrality is the sum of a node's ties.…”
The use of Twitter by scholars for public engagement is well‐established in the literature. However, fewer studies have looked into the composition of users engaged in scholarly communication on Twitter. This paper considers the degree to which Twitter chats are dominated by established versus emerging or amateur scholars through an investigation of #critlib, a hashtag around which library and information scholars and practitioners have congregated. #critlib Tweets over a period of three months are examined using social network analysis to determine the users most central to the conversation. The findings show that #critlib serves as an important space for emerging and amateur scholars to participate in the co‐creation of knowledge alongside experts in the field of LIS across a dispersed global network. The paper contributes to our knowledge of the #critlib community itself, suggests an impact of social media on the composition of scholarly communities, and deepens our understanding of networked participatory scholarship through the introduction of the idea of edge perspectives.
“…Scholarly communities on Twitter have received significant attention in the literature over the last few years. Specific fields have included science (Weller & Puschmann, 2011), education (Veletsianos, 2012;Veletsianos & Kimmons, 2016), and the digital humanities (Quan-Haase, et al, 2015;Grandjean, 2016). At the end of their study of Twitter use by education scholars, Veletsianos and Kimmons (2016) noted areas for future research, including "the comparison of traditional scholarly output measures to Twitter impact metrics [and] the analysis of role, gender, race, and age differences regarding hashtag use" (p. 9).…”
Section: Social Media and Scholarly Communicationmentioning
confidence: 99%
“…Centrality is not one thing, but a family of concepts (Borgatti, 2013). We specifically utilized degree centrality in determining the most prominent members of #critlib, following established conventions in similar studies (Grandjean, 2016;Riddell, et al, 2017;Tremayne, 2014). Degree centrality is the sum of a node's ties.…”
The use of Twitter by scholars for public engagement is well‐established in the literature. However, fewer studies have looked into the composition of users engaged in scholarly communication on Twitter. This paper considers the degree to which Twitter chats are dominated by established versus emerging or amateur scholars through an investigation of #critlib, a hashtag around which library and information scholars and practitioners have congregated. #critlib Tweets over a period of three months are examined using social network analysis to determine the users most central to the conversation. The findings show that #critlib serves as an important space for emerging and amateur scholars to participate in the co‐creation of knowledge alongside experts in the field of LIS across a dispersed global network. The paper contributes to our knowledge of the #critlib community itself, suggests an impact of social media on the composition of scholarly communities, and deepens our understanding of networked participatory scholarship through the introduction of the idea of edge perspectives.
“…Complex systems often generate emergent properties 2 which are not contained in an obvious way in its parts. Examples of such networks range over all disciplines of science, including the study of social media networks 3 , scientific collaboration networks 4 and the human brain and its interconnected neurons as a particularly interesting one. The interactions between the components of a complex system define a network of connections consisting of nodes and edges.…”
Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g. with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plug-in architecture in a multi-lingual way, integrating analyses in C#, Python and R and is freely available at http://www.perseus-framework.org.
“…It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks [18], message propagation in a social network service [19], friendship and acquaintance networks, collaboration graphs, kinship, disease transmission and sexual relationships [20,21]. These networks are often visualized through socio-grams, in which nodes are represented as points and ties are represented as lines.…”
Background: In the Internet of Things (IoT) firms, innovation beyond the border of a company is important. Furthermore, advantageous positioning in the innovation network is thought to enhance the result of innovation and ultimately contribute to profit. Objectives: The objective of this research is to clarify empirically the influence of the network structure among companies on innovation in the IoT field. Method: In this research, the relationship between the network structure and the result of innovation was analysed through social network analysis. Joint application patents related to the IoT companies were extracted from the intellectual property database. Results: As a result, the difference in the network structure of a company was related to the result of research and profitability. In particular, a company with a platform type of business model is considered highly profitable in the IoT business field. Conclusion: Drawing on an intellectual property database and employing social network analysis, this research quantifies the structure of innovation networks in terms of the results and operational efficiency of R&D.
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