First Steps to Netviz Nirvana: Evaluating Social Network Analysis with NodeXL

Bonsignore, Elizabeth; Dunne, Cody; Rotman, Dana; Smith, Marc A.; Capone, Tony; Hansen, Derek L.; Shneiderman, Ben

doi:10.1109/cse.2009.120

Cited by 43 publications

(26 citation statements)

References 18 publications

(27 reference statements)

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“…Given the greater accessibility of the blog (daily newsletter based) this and Twitter (with hashtag# change11) were chosen as data sources for this study. NodeXL, an open-source social network analysis tool (Bonsignore et al, 2009), that was used for the social network analysis in this study.…”

Section: Data Collection and Analysismentioning

confidence: 99%

How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

Wang

Anderson²,

Chen³

2018

IRRODL

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In this research paper, the authors analyse the collected data output during a 36 week cMOOC. Six-week data streams from blogs, Twitter, a Facebook group, and video conferences were tracked from the daily newsletter and the MOOCs' hashtag (#Change 11). This data was analysed using content analysis and social network analysis within an interpretative research paradigm. The content analysis was used to examine the technology learners used to support their learning while the social network analysis focused on the participant in different spaces and their participation patterns in connectivist learning.The findings from this research include: 1) A variety of technologies were used by learners to support their learning in this course; 2) Four types of participation patterns were reveled, including unconnected floaters, connected lurkers, connected participants, and active contributors. The participation of learners displays the participation inequality typical of social media, but the ratio of active contributors is much higher than xMOOCs; 3) There were five basic structures of social networks formed in the learning; and 4) The interaction around topics and topic generation supports the idea of learning as network creation after the analysis of participation patterns that are based on some deep interactive topic. The aim of this study is to gain insight into the behaviors of learners in a cMOOC in an open and distributed online environment, so that future MOOCs designers and facilitators can understand, design and facilitate more effective MOOCs for learners.

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Section: Data Collection and Analysismentioning

confidence: 99%

How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

Wang

Anderson²,

Chen³

2018

IRRODL

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“…Within the network, the lines connecting different government departments indicate previous cases of joint issuance of documents. The larger the size of a node, the more cases of previous joint issuance there have been (Bonsignore et al 2009;Mohammad et al 2009;De Liddo et al 2011;Hansen et al 2011), which highlights the significant role of that particular policy-making department in that field 6 (Huang et al 2015).…”

Section: Methodsmentioning

confidence: 99%

The Pattern of Policy Change on Disaster Management in China: A Bibliometric Analysis of Policy Documents, 1949–2016

Zhang

Zhong

et al. 2018

Int J Disaster Risk Sci

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This article presents a comprehensive review of China's policy system for the management of natural hazard-induced disasters from 1949 to 2016 through a quantitative bibliometric analysis of 5472 policy documents on such disasters. It identifies four phases of China's evolving disaster management system, which focused on agriculture, economic development, government and professional capacity building, and disaster governance, respectively. Characteristics of policies and contributing factors of policy change in each of the four phases are discussed in depth. This article provides a quantitative foundation for understanding the dynamic policy change of the disaster management system in China with a particular emphasis on the governance capacity and may serve as a basis for exploring the potential pathways of transformation according to the Sendai Framework for Disaster Risk Reduction 2015-2030 and the Sustainable Development Goals.

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“…We can give users rules to apply (e.g., [32], pp. 13), or teach them about basic readability principles [2]. However, it is challenging for users to balance (or even remember) these rules or optimize the ones that would most benefit the target task.…”

Section: Defining and Measuring Readabilitymentioning

confidence: 99%

“…One approach is to count the number of flaws that inhibit readability [27] and support analysts in reducing their prevalence [2,7,12].…”

Section: Defining and Measuring Readabilitymentioning

confidence: 99%

Interactive Network Exploration to Derive Insights: Filtering, Clustering, Grouping, and Simplification

Shneiderman

Dunne

2013

Graph Drawing

Self Cite

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Abstract. The growing importance of network analysis has increased attention on interactive exploration to derive insights and support personal, business, legal, scientific, or national security decisions. Since networks are often complex and cluttered, strategies for effective filtering, clustering, grouping, and simplification are helpful in finding key nodes and links, surprising clusters, important groups, or meaningful patterns. We describe readability metrics and strategies that have been implemented in NodeXL, our free and open source network analysis tool, and show examples from our research. While filtering, clustering, and grouping have been used in many tools, we present several advances on these techniques. We also discuss our recent work on motif simplification, in which common patterns are replaced with compact and meaningful glyphs, thereby improving readability.

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First Steps to Netviz Nirvana: Evaluating Social Network Analysis with NodeXL

Cited by 43 publications

References 18 publications

How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

The Pattern of Policy Change on Disaster Management in China: A Bibliometric Analysis of Policy Documents, 1949–2016

Interactive Network Exploration to Derive Insights: Filtering, Clustering, Grouping, and Simplification

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