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Infovis

Social media network maps and reports covered in the press

05MarMay 7, 2015 By Marc Smith

Coverage of our report on the six basic types of social media network structures created with the Pew Internet Research Center has been extensive. Here is a round up of the articles we have found about the study.

20140314-OnTheMedia-Twitter Cartography-Lee Rainie

Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia.
http://www.connectedaction.net/wp-content/uploads/2014/03/20140314-OnTheMedia-Twitter-Cartography-with-Lee-Rainie.mp3


20140220-WaPo-Pew-SMRF-6 Kinds of Twitter networks

Washington Post: The six types of conversations on Twitter


20140220-SFGate-Pew-SMRF-6 Kinds of Twitter networks

San Francisco Chronicle: The six ways we interact on Twitter


20140305-WVXU-Cincinnati-Pew-SMRF-6 Kinds of Twitter networks

http://www.connectedaction.net/wp-content/uploads/2014/03/20140305-WVXU-focus_PEW-SMRF-twitter_study-141.mp3

RADIO WVXU Cincinnati – Ann Thompson


20140228-MyFoxNY-Pew-SMRF-6 Kinds of Twitter networks

Fox News New York

 


20140220-AJAM-Pew-SMRF-6 Kinds of Twitter networks

Al Jazeera: Study maps Twitter’s information ecosystem

 


20140220-PBS Newshour Rundown-Pew-SMRF-6 Kinds of Twitter networks

PBS NewsHour: Study uncovers six basic types of Twitter conversations

 


20140220-DesMoines Register-Pew-SMRF-6 Kinds of Twitter networks

Des Moines Register: Twitter talk fits into 6 patterns, study finds

 


20140228-USAToday-Pew-SMRF-6 Kinds of Twitter networks

USAToday: Twitter talk fits into 6 patterns, study finds

 


20140220-NBCNews-Pew-SMRF-6 Kinds of Twitter networks

NBC: Liberals, Conservatives Tweet in Partisan Bubbles, Study Says

 


20140220-CNet-Pew-SMRF-6 Kinds of Twitter networks

CNET: Red state, blue state? On Twitter, never the twain shall meet

 


20140220-Time Entertainment-Pew-SMRF-6 Kinds of Twitter networks

TIME: Who Are TV’s Biggest Fans? New Research Names Twitter Users With the Most Influence

 


20140220-Quartz-Pew-SMRF-6 Kinds of Twitter networks

Quartz: Turns out Twitter is even more politically polarized than you thought

 


20140220-Forbes-Pew-SMRF-6 Kinds of Twitter networks

Forbes: These Charts Show Why Political Debate On Twitter Is Pointless

 


20140220-VatorNews-Pew-SMRF-6 Kinds of Twitter networks

Vator: Pew report: how we communicate on Twitter

 


20140220-GlobalNews CA-Pew-SMRF-6 Kinds of Twitter networks

Global News Canada: Study reveals six different types of conversations on Twitter

 


20140220-LiveScience-Pew-SMRF-6 Kinds of Twitter networks

Live Science: The 6 types of Twitter conversations revealed

 


20140220-SeattlePI-Pew-SMRF-6 Kinds of Twitter networks

Seattle PI: The six ways we interact on Twitter

 


20140220-AP-Pew-SMRF-6 Kinds of Twitter networks

Associated Press: Pew maps Twitter chatter in new type of study, finds 6 types of conversations

 


20140220-Redeye-Pew-SMRF-6 Kinds of Twitter networks

Chicago Tribune: The 5 cliques of Twitter

 


20140220-Mashable-Pew-SMRF-6 Kinds of Twitter networks

Mashable: Your Twitter Conversations Fall Into One of These Six Categories

 


20140220-PCMag-Pew-SMRF-6 Kinds of Twitter networks

PC Magazine: Which Type of Twitter Conversationalist Are You? In a recent report, Pew Researchers explain the six regularly observed types of conversation on Twitter

 


20140220-NPR-Pew-SMRF-6 Kinds of Twitter networks

NPR: Study: Conservatives And Liberals Rarely Debate On Twitter


20140220-DailyMail-Pew-SMRF-6 Kinds of Twitter networks

Daily Mail: What type of tweeter are you? Researchers reveal there are just SIX types of tweet

 


20140220-Diamondback-UMD-Pew-SMRF-6 Kinds of Twitter networks

The Diamond Back: Professor helps map social media connections

 


20140228-Forbes-Pew-SMRF-6 Kinds of Twitter networks

Your Social Media Conversation Is Like A Topographic Map

 


20140228-MediaPost-Pew-SMRF-6 Kinds of Twitter networks

Media Post

 


20140228-Politico-Pew-SMRF-6 Kinds of Twitter networks

Politico: How Twitter Works

 


20140228-UMD-Pew-SMRF-6 Kinds of Twitter networks

University of Maryland: New Map of Twitterverse finds 6 types of networks

 


20140228-UGA-Pew-SMRF-6 Kinds of Twitter networks

University of Georgia


2014-GovTech-6 Kinds of social media networks

GovTech


20140228-WiredIT-Pew-SMRF-6 Kinds of Twitter networks

Wired.IT


Posted in 2014, All posts, Foundation, NodeXL, Papers, Pew Internet, Research, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Sociology, Twitter Tagged 2014, 6 Types, Chart, graph, Information Visualization, Infovis, Map, network, NodeXL, Pew, Report, SMRF, Social Media, Social network, Social Structure, Twitter

January 31, 2014 – Kansas State University – Webinar: Social Media Maps and Measures with NodeXL

16JanMay 7, 2015 By Marc Smith

2014-Kansas State University - Logo

I will present a remote seminar at Kansas State University – on Jan. 31 about how to create social media maps and measures with NodeXL.

The remote presentation will take place 1-2:30 p.m. (Central Time) Friday, Jan. 31, in 301 Hale Library.  All are welcome to attend.

See: https://blogs.k-state.edu/it-news/2014/01/10/webinar-jan-31-charting-collections-of-connections-in-social-media-creating-maps-and-measures-with-nodexl/

This is a map of the network of 2,785 Twitter users whose recent tweets contained ““kansas state” OR KState” over the 1-day, 23-hour, 14-minute period from Monday, 13 January 2014 at 17:06 UTC to Wednesday, 15 January 2014 at 16:20 UTC.

 

Posted in 2014, All posts, Conference, Foundation, Measuring social media, Metrics, NodeXL, Presentation, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talks, Technology, University, Video, Visualization, Workshop Tagged 2014, Infovis, January, Kansas, KSU, Marc Smith, network, NodeXL, Seminar, SMRF, SNA, Social Media, Social Media Research Foundation, Training, University, Webinar

June 4, 2012 – ICWSM-12 – International Conference on Weblogs and Social Media – Dublin

21AprMay 7, 2015 By Marc Smith

On June 4th in Dublin, Ireland the 2012 International AAAI Conference on Weblogs and Social Media. ICWSM gathers computer scientists, linguists, communications scholars, and the social scientists to increase understanding of social media in all its incarnations.  Now in its sixth year, ICWSM is a leading venue for cutting-edge research in social media.

ICWSM-12, features a program of workshops, tutorials, contributed technical talks, posters and invited presentations.  The main conference features keynote talks from prominent social scientists and technologists.

Keynote
Keynote
Keynote
ANDREW TOMKINS
ENGINEERING DIRECTOR
GOOGLE+
PATRICK MEIER
DIRECTOR OF
CRISIS MAPPING AND PARTNERSHIPS
USHAHIDI
LADA ADAMIC
ASSOCIATE PROFESSOR
UNIVERSITY OF MICHIGAN
Andrew Tomkins is an engineering director at Google working on measurement, modelling, and analysis of content, communities, and users on the World Wide Web. Prior to joining Google, he spent four years at Yahoo! as chief scientist of search, and eight years at IBM’s Almaden Research Center, where he co-founded the WebFountain project. Andrew holds Bachelors degrees in Math and CS from MIT, and a PhD in CS from Carnegie Mellon University; he has published over a hundred technical papers. Patrick Meier is a recognized expert and thought leader on the intersection between new technologies, crisis early warning, humanitarian response and human rights.
He is the co-founder of the International Network of Crisis Mappers and previously co-directed Harvard University’s Program on Crisis Mapping and Early Warning. Over the past 10 years, Patrick has consulted extensively with several international organizations including the UN, OSCE and OECD in Africa, Asia and Europe. Patrick is also a distinguished scholar completing his PhD at The Fletcher School during which time he was a Doctoral Fellow at Stanford University. In 2010, President Bill Clinton publicly thanked him for his leadership and contributions. He blogs at iRevolution.net.
Lada A. Adamic is an associate professor in the School of Information and the Center for the Study of Complex Systems at the University of Michigan. She is also affiliated with EECS. Her research interests center on information dynamics in networks: how information diffuses, how it can be found, and how it influences the evolution of a network’s structure. Her projects have included identifying expertise in online question and answer forums, studying the dynamics of viral marketing, and characterizing the structure in blogs and other online communities. She has received an NSF CAREER award, and best paper awards from Hypertext ’08, ICWSM-10 and ICWSM-11, and the most influential paper of the decade award from Web Intelligence ’11.

ICWSM-12 will also hold a workshops and tutorials day just before the main conference.  Of the workshops, I am particularly interested in the Workshop on Social Media Visualization (SocMedVis) – http://socmedvis.ucd.ie/

“The goal of the workshop is to bring together researchers and industry practitioners interested in visual and interactive techniques for social media analysis, particularly in social sciences and humanities as well as in industry and to discuss ideas, techniques, and applications to support social media analysis.”

I will present a tutorial on Social Media Network Analysis with NodeXL on June 4th at the event:

MA2:
CHARTING COLLECTIONS OF CONNECTIONS IN SOCIAL MEDIA:
CREATING MAPS AND MEASURES WITH NODEXL

Marc Smith (marc@connectedaction.net)
9:00 AM – 12:00 PM

Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation’s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.

This network graph represents a network of 29 Twitter users whose recent tweets contained “icwsm”.  The network was obtained on Saturday, 21 April 2012 at 20:33 UTC.  There is an edge for each follows relationship.  There is an edge for each “replies-to” relationship in a tweet.  There is an edge for each “mentions” relationship in a tweet.  There is a self-loop edge for each tweet that is not a “replies-to” or “mentions”.  The earliest tweet in the network was tweeted on Saturday, 14 April 2012 at 18:55 UTC.  The latest tweet in the network was tweeted on Saturday, 21 April 2012 at 05:48 UTC.

The graph is directed.

The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph was laid out using the Harel-Koren layout algorithm.

The edge colors are based on relationship values.  The vertex sizes are based on followers values.

Top 10 Vertices, Ranked by Betweenness Centrality:
@icwsm
@johnbreslin
@IBMResearch
@CaptSolo
@marc_smith
@bde
@karenchurch
@imbenzene
@hemant_Pt
@_akisato

Overall Graph Metrics:
Vertices: 29
Unique Edges: 68
Edges With Duplicates: 32
Total Edges: 100
Self-Loops: 18
Connected Components: 5
Single-Vertex Connected Components: 4
Maximum Vertices in a Connected Component: 25
Maximum Edges in a Connected Component: 96
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.866455
Graph Density: 0.082512315270936
Modularity: 0.2488

Posted in All posts, Conference, Connected Action, Data Mining, Foundation, ICWSM, Measuring social media, NodeXL, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Sociology, Talks, Visualization Tagged 2012, Analysis, Conferece, Dublin, Facebook, ICWSM, Infovis, network, NodeXL, SNA, Social Media, Social Media Research Foundation, Tutorial, Twitter, Visualization, Weblogs, workshop

May 1st, 2012 – STM Innovations Seminar, Washington, D.C. – Reinventing Innovation – NodeXL workshop

21AprMay 7, 2015 By Marc Smith

May 1st, 2012 at the International Association of Scientific, Technical & Medical Publishers Innovations Seminar I will present an hour long training on:

Innovations networks in Social Media: Creating Maps and Measures with NodeXL

Speaker: Dr. Marc A. Smith, Chief Social Scientist, Connected Action Consulting Group

Know who is becoming more important than know how.  Networks are a data structure common found across all social media services that allow populations to author collections of connections.  Innovation networks are created when new connections form among people who have a portion of a solution.

The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts.  Applying the tool to a range of social media networks has already revealed the variations present in online social spaces.  A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented. In particular, innovation topics will be mapped to highlight the key people and groups talking about new ideas and opportunities.


Washington Marriott
1221 22nd Street, NW
Washington DC, USA

Posted in All posts, Conference, Data Mining, Foundation, Measuring social media, Metrics, NodeXL, Research, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talks, Visualization Tagged Analysis, Analytics, DC, Infovis, International Association of Scientific, Medical, network, NodeXL, Publishers, science, Seminar, SNA, Social Media, Social Media Research Foundation, STM, Technical, Technical & Medical Publishers, Washington

Contrasting teaparty and occupywallstreet twitter networks

16NovMay 7, 2015 By Marc Smith

Both teaparty and occupywallstreet are actively discussed in twitter.

This map of connections among people who tweeted Teaparty starts on 11/15/2011 14:22 UTC and ends on 11/15/2011 17:23, a total of 3 hours and 1 minute of traffic.

The Teaparty data set contained 1,533 tweets, replies and mentions.
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.

Top most between users:
@ronpaul
@michellemalkin
@christopherhull
@theteaparty_net
@capaction
@thedailyedge
@bill1phd
@dbargen
@gulagbound
@rightcandidates

Graph Metric: Value
Graph Type: Directed
Vertices: 659
Unique Edges: 8808
Edges With Duplicates: 1423
Total Edges: 10231
Self-Loops: 1084
Connected Components: 49
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 606
Maximum Edges in a Connected Component: 10148
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance:2.693965
Graph Density: 0.02036797
NodeXL Version: 1.0.1.193

The major clusters are composed of teaparty supporters. The center bottom cluster are teaparty critics.

This map of the connections among people who tweeted Occupywallstreet starts on 11/15/2011 23:08 and ends on 11/15/2011 23:34 UTC, a total of 26 minutes of traffic.

Occupywallstreet 1,370 tweets, replies and mentions
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.

Top most between users:
@occupywallst
@mmflint
@nyclu
@allisonkilkenny
@andrewbreitbart
@operationleaks
@occupydenver
@theatlantic
@usgeneralstrike
@rt_com

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3546
Edges With Duplicates: 826
Total Edges: 4372
Self-Loops: 794
Connected Components: 241
Single-Vertex Connected Components: 230
Maximum Vertices in a Connected Component: 747
Maximum Edges in a Connected Component: 3998
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.65438
Graph Density: 0.003246246
NodeXL Version: 1.0.1.194

Some notable contrasts:
Teaparty Graph Density: 0.002652645
Occupywallstreet Graph Density: 0.02036797 – significantly lower levels of interconnection
Teaparty: Single-Vertex Connected Components 44 of 1000
Occupywallstreet: Single-Vertex Connected Components 283 of 1000

Many more “isolates” (Single-Vertex Connected Components) in Occupywallstreet.
Many more hubs, and more retweeting activity in Occupywallstreet.

The difference in duration of these data sets illustrates the relative speed of content creation in the topics. The data sets are commensurable in that they are both the result of a single query against the Twitter search API. So both maps are the results of charting connections among the authors of the last 1500 tweets, how ever long that takes to create.

Posted in All posts, Collective Action, Connected Action, Foundation, Measuring social media, Network clusters and communities, Network metrics and measures, Network visualization layouts, NodeXL, Politics, Research, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Sociology, Technology, Visualization Tagged #occupywallstreet, 15, 2011, Chart, graph, Information Visualization, Infovis, Map, network map, Networks, NodeXL, November, SNA, Social Media, teaparty, Visualization

#JW11 NodeXL SNA Map for 10 October 2011

11OctMay 7, 2015 By Marc Smith

The Jive World 11 conference took place October 4-6, 2011.

[flickr id=”6234653454″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
These are the connections among the Twitter users who recently tweeted the word #JW11 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.jivesoftware.com/jiveworld

A larger version of the image is here: www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/ph…

Top most between users:
@jivesoftware
@gialyons
@mikefraietta
@cflanagan
@cosmopolitan_lv
@ginorossi
@kristinhersant
@thebrandbuilder
@alanlepo
@mor_trisha

Graph Metric: Value
Graph Type: Directed
Vertices: 345
Unique Edges: 3606
Edges With Duplicates: 2072
Total Edges: 5678
Self-Loops: 632
Connected Components: 11
Single-Vertex Connected Components: 9
Maximum Vertices in a Connected Component: 334
Maximum Edges in a Connected Component: 5659
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.353001
Graph Density: 0.034184361
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Posted in All posts, Conference, Industry, Measuring social media, Network visualization layouts, NodeXL, Social Media, Social Network Analysis, Social Roles, Sociology, Visualization Tagged 2011, Analysis, Analytics, BI, Conference, EventGraph, Infovis, Jive, network analysis, NodeXL, October, Platform, SNA, Social Media, Social network, Social Software, socialmedia, Twitter

September 22-23, 2011: Purdue University – Lecture on Social Media Networks

16SepMay 7, 2015 By Marc Smith

I will speak at Purdue University on September 22 and 23, 2011 about mapping social media networks.

My host is Sorin Matei, professor of Communications, who has been researching the social structure of social media networks.

I will also speak at Professor Matei’s class: COM 63200 On-line Interaction and Facilitation

Here is an example map of the connections among the people who tweeted the word “Purdue” on September 16th, 2011:

Connections among the Twitter users who recently tweeted the word Purdue when queried on September 16, 2011, scaled by numbers of tweets (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.purdue.edu/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

(Edges connecting users are bundled and curved with recent features added to NodeXL v.177.)

A larger version of the image is here: www.flickr.com/photos/marc_smith/6155750905/sizes/o/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@lifeatpurdue
@jajuanjohnson
@stfu_gabby
@purdueexponent
@charliienosheen
@mbrister2
@boilerfootball
@purduesports
@hipandresanbo
@cheesebrrrrr

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 4045
Edges With Duplicates: 706
Total Edges: 4751
Self-Loops: 977
Connected Components: 429
Single-Vertex Connected Components: 395
Maximum Vertices in a Connected Component: 528
Maximum Edges in a Connected Component: 4134
Maximum Geodesic Distance (Diameter): 12
Average Geodesic Distance: 3.517707
Graph Density: 0.003507508
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Posted in All posts, Collective Action, Common Goods, Community, Companies, Connected Action, Foundation, Industry, Measuring social media, Mobile Social Software, Network visualization layouts, NodeXL, Research, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Roles, Social Theories and concepts, Sociology, Talks, University, Visualization Tagged 2011, Indiana, Infovis, Lecture, Marc, Marc Smith, NodeXL, Presentation, Purdue, Smith, SMRF, SNA, Social Media, Social Media Research Foundation, Sociology, Talk, Trip, University, workshop

NodeXL (v.174) throws a curve: edges that bend (a bit)

13AugMay 7, 2015 By Marc Smith

Curved edges have arrived in NodeXL (v. 174).

Here is a network visualization with all the edges drawn as straight lines:

The same network graph can be drawn with slightly curved edges:

The result is a slight improvement in readability.

To start, the edges curve just a bit.  Soon, we will allow user control over the extent of the curvature.

Turn on curved edges by checking the box in the Graph Options dialog accessed from the graph pane.

Posted in All posts, Measuring social media, Network visualization layouts, NodeXL, SMRF, Social Media Research Foundation, Social network, Visualization Tagged 2011, August, Curve, Curved, Edges, Feature, Infovis, Link, network, NodeXL, SMRF, SMRFoundation, SNA, Social Media Research Foundation, Tie, update, Visualization

A legend in your own network graph: NodeXL legend describes data elements

12JunMay 7, 2015 By Marc Smith

Every network visualization should have a legend that explains what the colors, edge widths, and filters are that define the network graph.

NodeXL automatically generates a network legend and displays it when the Graph Elements menu is opened:

And the “Legend” option is selected.

This will place a legend at the bottom of the visualization canvas:

Posted in All posts, Foundation, Network visualization layouts, NodeXL, SMRF, Social Media Research Foundation, Social network, User interface, Visualization Tagged 2011, April, Chart, Design, Feature, graph, Infovis, Legend, Map, network, NodeXL, SMRF, SMRFoundation, Social Media Research Foundation, v.166, View, 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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