Analyzing Social Media Networks with Node: XL. Insights from by Derek L. Hansen, Ben Shneiderman and Marc A. Smith (Auth.)

By Derek L. Hansen, Ben Shneiderman and Marc A. Smith (Auth.)

Businesses, marketers, participants, and executive organisations alike want to social community research (SNA) instruments for perception into developments, connections, and fluctuations in social media. Microsoft's NodeXL is a loose, open-source SNA plug-in to be used with Excel. It presents fast graphical illustration of relationships of complicated networked info. however it is going extra than different SNA instruments -- NodeXL was once built via a multidisciplinary crew of specialists that compile details reviews, computing device technological know-how, sociology, human-computer interplay, and over two decades of visible analytic thought and data visualization right into a basic instrument someone can use. This makes NodeXL of curiosity not just to end-users but in addition to researchers and scholars learning visible and community analytics and their software within the genuine world.

In Analyzing Social Media Networks with NodeXL, individuals of the NodeXL improvement staff as much as supply readers with a radical and functional advisor for utilizing the software whereas additionally explaining the advance at the back of every one characteristic. mixing the theoretical with the sensible, this e-book applies particular SNA directions on to NodeXL, however the concept in the back of the implementation might be utilized to any SNA.

To research extra approximately reading Social Media Networks and NodeXL, stopover at the companion site at www.mkp.com/nodexl

*Walks you thru NodeXL, whereas explaining the speculation and improvement in the back of each one step, offering takeaways that could practice to any SNA

*Demonstrates how visible analytics learn could be utilized to SNA instruments for the mass marketplace

*Includes case reviews from researchers who use NodeXL on renowned networks like electronic mail, fb, Twitter, and wikis

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Extra info for Analyzing Social Media Networks with Node: XL. Insights from a Connected World

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Net I. Getting Started with Analyzing Social Media Networks 34 3. Social Network Analysis chapters will provide a guide to creating maps like these from Twitter and other social media platforms and data sources. For now, let’s consider the major components of a network in a bit more detail. 2 Vertices Vertices, also called nodes, agents, entities, or items, can represent many things. Often they represent people or social structures such as workgroups, teams, organizations, institutions, states, or even countries.

The simplest type of edge, an unweighted edge or binary edge, only indicates if an edge exists or not. For example, a friendship tie between Facebook users either exists or it does not. In contrast, a weighted edge includes values associated with each edge that indicate the strength or frequency of a tie. For example, a weighted edge between two Facebook users may indicate the number of photo comments exchanged or the duration of a friendship. Weighted edges are often represented visually as thicker or darker lines or as more or less opaque lines.

With 400 million users in 2010, many of whom are regularly active, Facebook contains one of the largest machine readable “social graphs” on earth. There are many ways people connect to one another in Facebook, from the obvious “friending” that starts a Facebook relationship, to the many ways people can subsequently interact by writing on one another’s “wall,” indicating that they “like” other people’s content, sending messages, tagging photos, and joining common fan clubs or groups. Facebook and related systems are rich sources of social network data as a result.

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