Monday, 28 November 2016

Introduction to NodeXL

Wasim Ahmed, from the University of Sheffield, is a PhD researcher in the Information School, and Research Associate at the Management School. Wasim is also a social media consultant, a part of Connected Action Consulting, and has advised security research teams, crisis communication intuitions, and companies ranked within the top 100 on the Fortune Global 500 list. Wasim often speaks at social media events, and is a regular contributor to the London School of Economics and Political Sciences (LSE) Impact blog. @was3210

This blog post is based on a conference with the same name which was delivered at the Introduction to Tools for Social Media Research conference. The slides for the talk can be found here. This blog post introduces and outlines some of the features of NodeXL.
Network Overview, Discovery, and Exploration for Excel (NodeXL) is a graph visualization tool which allows the extraction of data from a number of popular social media platforms including Twitter, YouTube, and Facebook with Instagram capabilities in beta. Using NodeXL it is possible to capture data and process it to generate a network graph based on a number of graph layout algorithms.
NodeXL is intended for users with little or no programming experience to perform Social Network Analysis. Social Network Analysis (SNA) is:
 “the process of investigating social structures through the use of network and graph theories” (Otte, Evelien, Rousseau, and Ronald, 2002)
Figure 1 below displays the connections between workers in an office:

Figure 1 – Graph of an example network graph















We can also think of the World Wide Web as a big network where pages are nodes and the links are edges. The Internet is also a network where nodes are computers and edges are physical connections between devices. Figure 2, below, from Smith, Rainie, Shneiderman, & Himelboim, 2014 provides a guide in contrasting patterns within network graphs.
The figure below shows that different topics on social media can have contrasting network patterns. For instance in the polarized crowd discussion one set of users may talk about Donald Trump and other about Hilary Clinton, in the unified crowd users may talk about different aspects of the election, and in brand clusters people may offer an opinion related to the election without being connected to one another and without mentioning each other. In a community cluster a group of users may talk about the different news articles surrounding Hilary Clinton. Broadcast networks are typically found when analysing news accounts as these disseminate news which is retweeted by a large amount of users. We can think of support networks as those accounts which reply to a large number of accounts, we can think of the customer support of a bank which may reply to a large amount of Twitter users

Figure 2 - Six types of network structure diagram




























NodeXL can also generate a number of metrics associated with the graphs such as the most frequently shared URLs, Domains, Hashtags, Words, Word Pairs, Replied-To, Mentioned Users, and most frequent tweeters These metrics are produced overall and also by group of Twitter users. By looking at different metrics associated with different groups (G1, G2, G3 etc) you can see the different topics that users may be talking about.
NodeXL also hosts a graph gallery where users can upload workbooks and network graphs. However, in regards to ethics in an academic context uploading to the graph gallery may not be permitted as participants will be personally identifiable. However, it is possible to use NodeXL to create offline graphs and to report aggregately. 

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