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UMD

University of Maryland Computer Science Class (CMSC734) Student Projects Put NodeXL to Work: Finding Insights in Diverse Networks

11DecMay 7, 2015 By Marc Smith

Screen Shot 2013-12-11 at 6.11.17 PM

NodeXL

Graduate students in Computer Science at the University of Maryland in a class on information visualization produced a striking variety of NodeXL network analysis visualizations for their recent homework projects.  The class, taught by Prof. Ben Shneiderman (www.cs.umd.edu/~ben), covers commercial tools, such as Spotfire and Tableau, and research software, giving students a chance to learn a range of existing visualization techniques and tools.  The NodeXL homework project is done by individual students, midway in the semester, while 5-person student teams are also busy working on their major term projects, which create novel visualization tools for specialized applications.  To see all the projects, click:

https://wiki.cs.umd.edu/cmsc734_f13/index.php?title=Homework_Number_2

(Don’t be deterred by security warnings, the class wiki is open for all to read, but only students can edit)!

Several of the 30 projects deal with Facebook, Twitter, email, Wikipedia, and YouTube social networks, with academic citation patterns and sports networks adding variety.  Entertainment, finance and medical analyses round out the collection, showing the huge range of potential NodeXL applications.  Students had only two weeks to find data, import it, clean it, and then create meaningful visualizations that enabled them to find interesting insights into connected structures.

Gregory Kramida’s analysis of stock symbol co-occurrences in financial articles

Gregory Kramida analyzed the connections among company names in the business press.  See:

https://wiki.cs.umd.edu/cmsc734_f13/images/9/9f/Analysis_of_Stock_Symbol_Co-occurences_in_Financial_Articles.pdf

The project shows the strong linkages between technology companies and consumer services, finance and public utilities.  The data set of more than 50,000  financial articles had more than 400,000 co-occurrences of stock ticker symbols.   He used the NodeXL grouping feature to organize the stocks into groups by industry and then showed results using the Group-in-a-Box layout feature.  This network is limited to companies that were mentioned together at least 50 times.

2013-UMD-CS-NodeXL-Kramida

Ruofei Du’s analysis of co-authorship patterns

Ruofei Du probed the relationship among authors in 1033 scientific papers from the 1988 to 2013 User Interface Software & Technology (UIST) conference. See:

https://wiki.cs.umd.edu/cmsc734_f13/images/b/bd/Uist_viz2.pdf

The co-author collaborations followed commonly seen patterns of professors and their students, but the relationships between academia and industry showed novel patterns.  After grouping authors by their organizations, it is apparent that Microsoft is well-represented at this conference through numerous collaborations with universities.

2013-UMD-CS-NodeXL-Du

Joshua Brule’s analysis of actor co-performance connections from the television series Firefly 

Joshua Brule created an intriguing story of television and film actors and actresses that emerges from analysis of ten actors from the cancelled television series Firefly. See:

https://wiki.cs.umd.edu/cmsc734_f13/images/7/76/Firefly.pdf

The actors had few collaborations before appearing on the program, but many afterwards.  The carefully constructed bipartite network shows how ten actors collaborated in 38 films, television shows, or videogames.

2013-UMD-CS-NodeXL-Brule

 

Posted in 2013, All posts, Foundation, Maryland, Metrics, NodeXL, Presentation, Research, SMRF, SNA, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Sociology, University, Visualization Tagged Analysis, CS, Maryland, network, NodeXL, Shneiderman, SMRF, SNA, Social Media Research Foundation, UMD, University, Visualization

July 28 – August 1, 2013 – DSST (Digital Societies and Social Technologies) Summer Institute @ University of Maryland – NodeXL Training

11JunMay 7, 2015 By Marc Smith

2013-Marc at UMDUMD CASCI Logo UMD HCIL LogoUMD iSchool_LOGO2011-SMRF-Small Logo

July 28 – August 1, 2013
DSST 2013 Digital Societies and Social Technologies Summer Institute: NodeXL Training

University of Maryland — College Park, Maryland USA

I will be teaching a workshop on Thursday August 1st on using NodeXL for social media network analysis at the upcoming 2013 Digital Societies and Social Technologies Summer Institute at the University of Maryland.  The Institute is devoted to training researchers in methods and theory that can help frame research into the social impacts of information technology:

MOOCs, Education and learning; personal health and well-being; open innovation, eScience, and citizen science; co-production, open source, and new forms of work; cultural heritage and information access; energy management and climate change; civic hacking, engagement and government; disaster response; cybersecurity and privacy – these are just a few problem domains where effective design and robust understanding of complex sociotechnical systems is critical.  To meet these challenges a trans-disciplinary community of scholars has come together from fields as wide ranging as CSCW, HCI, social computing, organization studies, information visualization, social informatics, sociology, information systems, medical informatics, computer science, ICT for development, education, learning science, journalism, and political science.

For more information about the Summer Institute, contact the Summer Institute co-coordinators, Brian Butler (bsbutler@umd.edu) and Susan Winter (sjwinter@umd.edu).  For information about the broader community of researchers interested in design and study of sociotechnical systems, see:  CSST (www.sociotech.net), Social Webshop (http://www.cs.umd.edu/hcil/webshop2012/), the “Researchers of the Socio-Technical” Facebook group, or the CSST listserv (csst@listserv.syr.edu).

Here are the slides from my talk:

2013 NodeXL Social Media Network Analysis from Marc Smith
Posted in 2013, All posts, Maryland, Measuring social media, Metrics, NodeXL, Presentation, Research, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Sociology, Talk, Talks, University, Visualization Tagged 2013, CASCI, DSST, HCI, HCIL, Maryland, network, NodeXL, SNA, Social network, Training, Tutorial, UMD, University of Maryland, Visualization, workshop 2 Comments

Paper: Tech Report at University of Maryland on EventGraphs

08JulMay 7, 2015 By Marc Smith

A new paper on visualizing social media has been released on the University of Maryland, Human Computer Interaction Laboratory tech report archive.  Co-authored by Derek Hansen,  myself, and Ben Shneiderman, the paper describes and visualizes the patterns of connections formed when people tweet about events like conferences and news stories.

EventGraphs_2010_HCIL_Tech_Report

http://www.cs.umd.edu/localphp/hcil/tech-reports-search.php?number=2010-13

Hansen, D., Smith, M., Shneiderman, B.
EventGraphs: Charting Collections of Conference Connections
HCIL-2010-13

EventGraphs are social media network diagrams constructed from content selected by its association with time-bounded events, such as conferences. Many conferences now communicate a common “hashtag” or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The paper also identifies the structural and conversational patterns to look for and highlight in EventGraphs and provides design ideas for their improvement.

Posted in All posts, Community, Connected Action, Maryland, Measuring social media, NodeXL, Papers, Research, Social Interaction, Social Media, Social network, Social Network Analysis, Sociology, Twitter, University, Visualization Tagged 2010, Analysis, Chart, EventGraph, graph, HCIL, June, Maryland, network, NodeXL, Report, SMRF, SMRFoundation, SNA, social, Social Media Research Foundation, Tech, UMD, University, Visualization

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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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