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Ben Shneiderman

Pew Internet and Social Media Research Foundation Report: Six kinds of social media networks in Twitter

20FebMay 7, 2015 By Marc Smith

2013-Pew Banner Logo

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Working together,  the Pew Internet and American Life Project and the Social Media Research Foundation has published a report on the variations in social media crowd structures documented by network analysis and visualization of Twitter. The report is titled:

Mapping Twitter Topic Networks:
From Polarized Crowds to Community Clusters

The paper documents the distinct patterns of connection that emerge when people talk to one another using social media services like Twitter.  The paper includes six network visualizations that clearly demonstrate the diverse ways people connect to people when using online tools.

2014-Pew-SMRF-NodeXL-6 Kinds of social media network patterns - Animated

The report was produced by Marc Smith from the Social Media Research Foundation, Lee Rainie from the Pew Research Center’s Internet & American Life Project, Itai Himelboim professor of communications at the University of Georgia, and Ben Shneiderman professor of computer science from the University of Maryland.

Posted in 2014, All posts, Collective Action, Companies, Connected Action, Foundation, Measuring social media, Metrics, NodeXL, Papers, Pew Internet, Politics, Research, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Twitter, University, Visualization Tagged American Life, Analysis, Ben Shneiderman, Big Data, Bigdata, Internet, Itai Himelboim, Lee Rainie, Marc Smith, network, network analysis, NodeXL, Pew, Political Network, Politics, SMRF, Social Media Research Foundation, Social Structure, Visualization

Book: Flier and Cover Art – Analyzing social media networks with NodeXL: Insights from a connected world

19JunMay 7, 2015 By Marc Smith

The production team at Morgan-Kaufmann have created a cover and a flier for the forthcoming book:

2010 – June – NodeXL Book Flyer.

Written and edited by Derek Hansen, Ben Shneiderman and Marc Smith, the book contains contributed chapters on sample social media systems:

[Chapter 10]: Twitter: Conversation, Entertainment and Information, All in One Network!

By Vladimir Barash and Scott Golder

[Chapter 11]: Visualizing and Interpreting Facebook Networks

By Bernie Hogan

[Chapter 12]: WWW Hyperlink Networks

By Robert Ackland

[Chapter 13]: Flickr: Linking People, Photos, and Tags

By Eduarda Mendes Rodrigues and Natasa Milic-Frayling

[Chapter 14]: YouTube: Contrasting Patterns of Interaction and Prominence

By Dana Rotman and Jennifer Golbeck

[Chapter 15]: Wiki Networks: Networks of Creativity and Collaboration

By Howard T Welser, Patrick Underwood, Dan Cosley, Derek Hansen, and Laura Black

This handy poster contains many details about the book contributors, chapters, and the book cover (which you can also see below):

2010 - Book - Analyzing Social Media Networks with NodeXL Cover

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

Posted in All posts, Book, Collective Action, Common Goods, Community, Connected Action, Maryland, Measuring social media, Metrics, Network clusters and communities, Network data providers (spigots), Network metrics and measures, Network visualization layouts, NodeXL, Performance scale parallel and cloud computing, Research, Social Media, Social network, Social Network Analysis, Social Roles, Sociology, University, User interface, Visualization Tagged 2010, Art, Ben Shneiderman, Book, Chart, class, Cover, Derek Hansen, Flier, graph, Hansen, learn, Map, Marc Smith, Maryland, Morgan Kaufmann, NodeXL, Promotional materials, Shneiderman, SNA, Social Media, social network analysis, teach, textbook

Book in progress: “Analyzing Social Media Networks with NodeXL: Insights from a Connected World”

30JanMay 7, 2015 By Marc Smith

2009 - November - Morgan Kaufmann Logo

Along with Professors Ben Shneiderman (Computer Science/Human Computer Interaction Lab) and Derek Hansen (College of Information Studies) from the University of Maryland I am writing and editing a book about analyzing the social media networks that form whenever people link or reply to one another, favorite, rate, read, or edit data about other people or their objects.  Social media networks can be analyzed using the methods of social network analysis, the mathematical application of graph and network theory to the social sciences.  Using social network analysis collections of connections can be analyzed and compared to identify key people and groups and measure changes over time and following interventions.

2009 - December - Elsevier Logo

I am pleased to announce that we have signed with Elsevier/ Morgan Kaufmann to produce a book: Analyzing Social Media Networks with NodeXL: Insights from a Connected World for a Summer 2010 delivery!

2009 - October - NodeXL Facebook Network Marc Smith

A map of the relationships among the population of people who all tweet a particular keyword can lead to the discovery of the key hubs and influential people in the network.  A social network analysis of reply patterns in email collections displays clusters around projects and highlights key people and relationships.  Visualizing the connections among your friends in Facebook can reveal the various life stages and communities in which you have participated.  When you chart the links between videos and users in YouTube content with interesting network properties is exposed based on well connected content creators and influential commentators.  A graph of  the individual connections between flickr users illustrates the emergent formation of groups around social networks, locations, and topics.

These kinds of social media network data collection, scrubbing, analysis, and display tasks have historically required a remarkable collection of tools and skills.  A great example of the variety of tools that can be used in concert to extract, analyze and display social media networks can be found on Drew Conway’s blog.  This is a powerful set of tools for those who can master the demands of python and API interfaces.  In contrast, the approach the NodeXL project has taken is to provide an end-user GUI application environment built within the framework of Excel 2007 for performing basic social media network analysis and visualization for non-programmers.  The python path is certainly the high road for experts and those with demanding volumes or esoteric data requirements.  But for the non-coding user, NodeXL may be one of the easiest ways to both manipulate network graphs and get graphs from a variety of social media sources.

There are already some materials available to guide new users interested in learning about NodeXL, social networks, and social media.  A video tutorial for NodeXL demonstrates the extraction of the network of people in twitter who mentioned the term “digg”.  A tutorial guide to NodeXL offers a step by step guide to features in the NodeXL toolkit (with supporting data sets).  But the book will capture the theory, history, domain and process of social media network analysis in a single volume.

The volume contains a broad introduction to social media, social networks and the operation of the NodeXL application and then features a series of  chapters from leading researchers that focus on a particular social media system (email, Facebook, Twitter, YouTube, flickr, Wikis, the WWW hyperlink network) and the networks each contains (replies, friends, follows, subscribes, comments, favorites, edits, links, etc).   A final chapter outlines a programmer’s view of the NodeXL code, in contrast to the code-free approach of the remainder of the book.

Our intended audience is the mostly non-programming population that is interested in social media and the techniques of social network analysis.  The volume is largely in the form of a how-to guide that readers can follow and replicate all examples.  Using your own free and open copy of NodeXL, you will be able to use sample data sets or create similar live queries that map relationships in social media systems.

We have an ambitious production schedule so the book may be on a book store shelf or online retailer search result in summer 2010.

Table of contents…

Continue reading →

Posted in All posts, Book, NodeXL, Research, Social Media, Social network, Visualization Tagged Analyzing, Ben Shneiderman, Book, Chart, Derek Hansen, graph, Marc Smith, Morgan Kaufmann, network, Networks, NodeXL, SNA, Social Media, Visualization 4 Comments

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Transparency in Social Media

2015-07-30-Transparency in Social Media-Structures of Twitter Crowds and COnversations
Transparency in Social Media
Sorin Adam Matei, Martha G. Russell, Elisa Bertino

CÓMO ENCONTRAR LOS HASHTAGS MÁS POTENTES: Para convertir LEADS a VENTAS (SEOHashtag nº 1) (Spanish Edition)

Apply NodeXL in espanol!

CÓMO ENCONTRAR LOS HASHTAGS MÁS POTENTES - Para convertir LEADS a VENTAS (SEOHashtag nº 1) (Spanish Edition)
By: Vivian Francos from #SEOHashtag Comparto algunas de las mejores formas de elegir los hashtags más poderosos y
que puedan generar tráfico a tus redes sociales para aprovechar el poder del
hashtag.
Si quieres aumentar tus interacciones, debes aprender a utilizar los hashtags como herramienta.

https://amzn.to/305Hpsv

Networked


Networked By Lee Rainie and Barry Wellman

Social Media in the Public Sector

2015-07-31Social Media in the Public Sector-Cover
Ines Mergel

Ways of Knowing in HCI

2014-Ways of Knowing in HCI - Olson and Kellogg

The Virtual Community


Virtual Community

The Evolution of Cooperation


The Evolution of Cooperation

Governing the Commons


Governing the Commons

SmartMobs


SmartMobs

Networks, Crowds, and Markets


Networks, Crowds, and Markets

Development of Social Network Analysis


Development of Social Network Analysis: A Study in the Sociology of Science

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