Analyzing Social Media Networks with NodeXL: Insights from a Connected World

Analyzing Social Media Networks with NodeXL: Insights from a Connected World

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Elsevier Science


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Analyzing Social Media Networks with NodeXL: Insights from a Connected World

Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology. This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies.

Part I provides background in the history and concepts of social media and social networks. Also included here is social network analysis, which flows from measuring, to mapping, and modeling collections of connections. The next part focuses on the detailed operation of the free and open-source NodeXL extension of Microsoft Excel, which is used in all exercises throughout this book. In the final part, each chapter presents one form of social media, such as e-mail, Twitter, Facebook, Flickr, and Youtube. In addition, there are descriptions of each system, the nature of networks when people interact, and types of analysis for identifying people, documents, groups, and events.

  • Walks you through NodeXL, while explaining the theory and development behind each step, providing takeaways that can apply to any SNA
  • Demonstrates how visual analytics research can be applied to SNA tools for the mass market
  • Includes case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and wikis
  • Download companion materials and resources at

Product Details

ISBN-13: 9780123822291
Publisher: Elsevier Science
Publication date: 09/29/2010
Pages: 304
Product dimensions: 8.40(w) x 10.80(h) x 0.80(d)

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By Derek L. Hansen Ben Shneiderman Marc A. Smith


Copyright © 2011 Elsevier Inc.
All right reserved.

ISBN: 978-0-12-382230-7

Chapter One

Introduction to Social Media and Social Networks


1.1 Introduction 3 1.2 A Historical Perspective 3 1.3 The Rise of Social Media as Consumer Applications 4 1.4 Individual Contributions Generate Public Wealth 5 1.5 Who Should Read This Book 5 1.6 Applying Social Media to National Priorities 6 1.7 Worldwide Efforts 7 1.8 Practitioner's Summary 8 1.9 Researcher's Agenda 8


Billions of people create trillions of connections through social media each day, but few of us consider how each click and key press builds relationships that, in aggregate, form a vast social network. Passionate users of social media tools such as email, blogs, microblogs, and wikis eagerly send personal or public messages, post strongly felt opinions, or contribute to community knowledge to develop partnerships, promote cultural heritage, and advance development. Devoted social networkers create and share digital media and rate or recommend resources to pool their experiences, provide help for neighbors and colleagues, and express their creativity. The results are vast, complex networks of connections that link people to other people, documents, locations, concepts, and other objects. New tools are now available to collect, analyze, visualize, and generate insights from the collections of connections formed from billions of messages, links, posts, edits, uploaded photos and videos, reviews, and recommendations. As social media have emerged as a widespread platform for human interaction, the invisible ties that link each of us to others have become more visible and machine readable. The result is a new opportunity to map social networks in detail and scale never before seen. The complex structures that emerge from webs of social relationships can now be studied with computer programs and graphical maps that leverage the science of social network analysis to capture the shape and key locations within a landscape of ties and links. These maps can guide new journeys through social landscapes that were previously uncharted.


Network science focuses on the study of patterns of connection in a wide range of physical and social phenomena. Network researchers have explored foundational physical systems created by chemical and genetic connections, webs of consumption of which animals eat which others, and profound distributed human social phenomena such as collective action, empathy, social cohesion, privacy, responsibility, markets, motivation, and trust. In the past few decades, network researchers have developed new data collection methods, innovative mathematical techniques, and surprising predictive theories. Just as Lord Kelvin (1824–1907) encouraged careful measurement as the method of advancing science, the new sciences of collective action, collaboration, and productive communities require new forms of measurement. Similarly, where Newton (1643–1727) and Leibniz (1646–1716) created the mathematical methods of calculus to grasp the physical world of objects in motion, social scientists are developing advanced mathematical methods for capturing social network evolution, diffusion, and decay. Like Galileo's telescope (1564–1642), Hooke's microscope (1635–1703), or Roentgen's (1845–1923) x-rays, new information analysis tools are creating visualizations of never before seen structures. Jupiter's moon, plant cells, and the skeletons of living creatures were all revealed by previous technologies. Today, new network science concepts and analysis tools are making isolated groups, influential participants, and community structures visible in ways never before possible.

Social network analysis is the application of the broader field of network science to the study of human relationships and connections. Social networks are primordial; they have a history that long predates systems like Facebook and Friendster, and even the first email message. Ever since anyone exchanged help with anyone else, social networks have existed, even if they were mostly invisible. Social networks are created from any collection of connections among a group of people and things. Social network science is itself relatively new, with roots in the early twentieth century that built on two centuries of work in the mathematics of graphs and topology. In the twenty-first century, network science has blossomed alongside a new global culture of commonplace networked communications. With widespread network connectivity, within just the past few decades, billions of people have changed their lives by creatively using social media. We use social media to bring our families and friends closer together, reach out to neighbors and colleagues, and invigorate markets for products and services. Social media are used to create connections that can bind local regions and span continents. These connections range from the trivial to the most valued, potent collaborations, relationships, and communities. Social media tools have been used successfully to create large-scale successful collaborative public projects like Wikipedia, open source software used by millions, new forms of political participation, and scientific collaboratories that accelerate research. Unheard of just a few years ago, today systems such as blogs, wikis, Twitter, and Facebook are now headline news with social and political implications that stretch around the globe. Despite the very different shapes, sizes, and goals of the institutions involved in social media, the common structure that unifies all social media spaces is a social network. All of these systems create connections that leave traces and collectively create networks.


Social media are visible in the form of consumer applications such as Facebook and Twitter, but significant use of social media tools takes place behind the firewalls that surround most corporations, institutions, and organizations. Inside these enterprises employees share documents, post messages and engage in extensive discussions, document annotation, and create extensive patterns of connections with other employees and other resources. Networked communication has become an indispensable link to customers and partners and a critical internal nervous system required for every aspect of commerce. Social media tools cultivate the internal discussions that improve quality, lower costs, and enable the creation of customer and partner communities that offer new opportunities for coordination, marketing, advertising, and customer support.

As enterprises adopt tools like email, message boards, blogs, wikis, document sharing, and activity streams, they generate a number of social network data structures. These networks contain information that has significant business value by exposing participants in the business network who play critical and unique roles. Some employees act as bridges or brokers between otherwise separated segments of the company. Others have patterns of connection that indicate that they serve as sources of information for many others. Social network analysis of organizations offers a form of MRI or x-ray image of the organizational structure of the company. These images illuminate the ways the members of the organization are actually structured in contrast to the formal hierarchies of traditional "org-charts."

Technology consulting firms have recently started to highlight the value of analyzing patterns of connection within an organization. The Gartner Group reported that social network analysis would prove to be a strategic advantage for a corporation, calling it an "untapped information asset." They recommend the analysis of "business intelligence on the ties, information flows and value exchanges" within a corporation. Network analysis can be focused, they argue, on three separate regions of commerce: organizational network analysis, value network analysis, and influence analysis, which map loosely to internal, vendor, and consumer populations. In each segment, network analysis is a useful method for identifying choke points and positions of leverage, locating expertise, and enhancing innovation.


Social media collective goods are a remarkable story of bottom-up individual initiative that leads to the creation of public value and wealth. Collections of individual social media contributions can create vast, often beneficial, yet complex social institutions. The intriguing challenge for the authors of this book and for a growing circle of social media analysts is to focus on individual behaviors while recognizing the emergent, collective properties of social media contributions. Seeing the social media forest, and not just the trees, branches, and leaves, requires tools that can assemble, organize, and present an integrated view of large volumes of records of interactions. Building a better view of the social media landscape of connection can lead to improved user interfaces and policies that increase individual contributions and their quality. It can lead to better management tools and strategies that help individuals, organizations, and governments to more effectively apply social media to their priorities.

Many utopian commentators have reported and proclaimed the benefits of social media. However, dangerous criminals, malicious vandals, promoters of racial hatred, and oppressive governments can also use social media tools to enable destructive activities. Critics of social media warn of the dangers of lost responsibility and respect for creative contributions, when vital resources are assembled from many small pieces. These dangers heighten interest in understanding how social media phenomena can be studied, improved, and protected. Why do some groups of people succeed in using these tools while many others fail? Community managers and participants can learn to use social network maps of their social media spaces to cultivate their best features and limit negative outcomes. Social network measures and maps can be used to gain insights into collective activity and guide optimization of their productive capacity while limiting the destructive forces that plague most efforts at computer-mediated communications. People interested in cultivating these communities can measure and map social media activity in order to compare and contrast social media efforts to one another.

Around the world, community stakeholders, managers, leaders, and members have found that they can all benefit from learning how to apply social network analysis methods to study, track, and compare the dynamics of their communities and the influence of individual contributions. Business leaders and analysts can study enterprise social networks to improve the performance of organizations by identifying key contributors, locating gaps or disconnections across the organization, and discovering important documents and other digital objects. Marketing and service directors can use social media network analysis to guide the promotion of their products and services, track compliments and complaints, and respond to priority customer requests. Community managers can apply these techniques to public-facing systems that gather people around a common interest and ensure that socially productive relationships are established. Social media tools have become central to national priorities requiring government agency leaders to become skillful in building and managing their communities and connections. Governments at all levels must learn to optimize and sustain social media tools for public health information dissemination, disaster response, energy conservation, environmental protection, community safety, and more.


Excerpted from ANALYZING SOCIAL MEDIA NETWORKS WITH NODEXL by Derek L. Hansen Ben Shneiderman Marc A. Smith Copyright © 2011 by Elsevier Inc.. Excerpted by permission of MORGAN KAUFMANN. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

Table of Contents

I. Getting Started with Analyzing Social Media Networks

  1. Introduction to Social Media and Social Networks
  2. Social media: New Technologies of Collaboration
  3. Social Network Analysis: Measuring, Mapping, and Modeling Collections of Connections

II. NodeXL Tutorial: Learning by Doing

4. Getting Started with NodeXL, Layout, Visual Design, and Labeling 5. Calculating and Visualizing Network Metrics 6. Preparing Data and Filtering 7. Clustering and Grouping

III Social Media Network Analysis Case Studies

8. Email: The Lifeblood of Modern Communication 9. Thread Networks: Mapping Message Boards and Email Lists 10. Twitter: Conversation,Entertainment, And Information, All in One Network!- Scott Golder, Cornell University,Vladimir Barash, Cornell University 11. Visualizing and Interpreting Facebook Networks- Bernie Hogan, Oxford Internet Institute 12. WWW Hyperlink Networks- Robert Ackland, ANU 13. Flickr: Linking People, Photos, and Tags- Eduarda Mendes Rodrigues, Microsoft Research, Natasa Milic-Frayling, Microsoft Research 14. YouTube: Contrasting Patternsof Interaction and Prominence- Dana Rotman and Jennifer Golbeck 15. Wiki Networks: Connections of Creativity and Collaboration- Ted Welser, University of Maryland, Derek Hansen, University of Maryland Appendix- NodeXL for Programmers- Tony Capone

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