Hello North Bay Area network analysis fans!
Join the NodeXL users meetup at the Napa Embassy Suites hotel (1075 California Boulevard, Napa, California, 94559, USA) on the 27th of February at 6pm.
Meet other network, social network, and social media network researchers and practitioners.
Hear about the latest and upcoming updates in the free and open NodeXL application.
Connect to the NodeXL network! Please RSVP using the form below:
RADIO WVXU Cincinnati – Ann Thompson interviews Marc Smith about the recent publication of a report on social media networks in Twitter co-authored with the Pew Internet Research Center.
“How millions of tweets boil down to six types of conversations“
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.
Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia.
Washington Post: The six types of conversations on Twitter
San Francisco Chronicle: The six ways we interact on Twitter
RADIO WVXU Cincinnati – Ann Thompson
Al Jazeera: Study maps Twitter’s information ecosystem
PBS NewsHour: Study uncovers six basic types of Twitter conversations
Des Moines Register: Twitter talk fits into 6 patterns, study finds
USAToday: Twitter talk fits into 6 patterns, study finds
NBC: Liberals, Conservatives Tweet in Partisan Bubbles, Study Says
CNET: Red state, blue state? On Twitter, never the twain shall meet
TIME: Who Are TV’s Biggest Fans? New Research Names Twitter Users With the Most Influence
Quartz: Turns out Twitter is even more politically polarized than you thought
Forbes: These Charts Show Why Political Debate On Twitter Is Pointless
Vator: Pew report: how we communicate on Twitter
Global News Canada: Study reveals six different types of conversations on Twitter
Live Science: The 6 types of Twitter conversations revealed
Seattle PI: The six ways we interact on Twitter
Associated Press: Pew maps Twitter chatter in new type of study, finds 6 types of conversations
Chicago Tribune: The 5 cliques of Twitter
Mashable: Your Twitter Conversations Fall Into One of These Six Categories
NPR: Study: Conservatives And Liberals Rarely Debate On Twitter
Daily Mail: What type of tweeter are you? Researchers reveal there are just SIX types of tweet
The Diamond Back: Professor helps map social media connections
Your Social Media Conversation Is Like A Topographic Map
University of Maryland: New Map of Twitterverse finds 6 types of networks
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.
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.
The INSNA Sunbelt social network conference will be held February 18-23 at the TradeWinds Island Resort on the island of St. Pete Beach.
There will be NodeXL related talks at the conference.
NodeXL: Network Analysis Made Simple
Tuesday February 18, 8:00am – 11:00am & 11:30am – 2:30pm
Marc Smith, Social Media Research Foundation
CITRUS Ballroom
Twitter Conversations as Network Structures: Typology and Measurements
Saturday February 22,
Itai Himelboim, Marc Smith, Ben Shneiderman, Lee Rainie
The conference schedule is available.
I hope to see you at the conference!
I will present at the 2014 Strata Conference in Santa Clara, CA on February 11, 2013.
Network Science Made Simple: SNA for pie chart makers
I will also hold Office Hours at the event:
Office Hour with Marc Smith (Team NodeXL)
Above is a map of the connections among the people who recently tweeted the term “strataconf” over the 7-day, 19-hour, 38-minute period from Sunday, 26 January 2014 at 21:53 UTC to Monday, 03 February 2014 at 17:32 UTC. The key people in the network at this point are:
You can make these types of maps with just a few clicks using NodeXL.
I hope to see you in Santa Clara!
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.
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.
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/
(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:
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.
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/
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.
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/
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.
Network analysis is a way of looking at the world that focuses on the shape and structure of collections of relationships.
In a network perspective the world is not primarily composed of individuals (“nodes”, “vertices”, “entities”). Instead, a network approach focuses on relationships between individuals (“edges”, “ties”, “connections”, “links”).
When collections of connections are analyzed, network patterns emerge. Networks have a variety of shapes and within them people occupy a variety of locations within each network. Some people are highly connected, while most people have just a few connections, for example.
Network theory provides a big collection of math that enables the measurement of these shapes and structures.
Using these measures, network analysis can identify key people in important locations in the network (for example: hubs, bridges, and islands). Network metrics allow for the network as a whole to be measured in terms of size and shape. Networks have many basic shapes and we have found six shapes to be common in internet and enterprise social media: divided, unified, fragmented, clustered, outward hub and spoke, inward hub and spoke. These shapes are created when people make individual decisions about who to reply to, link to, and like.
Divided networks are created when two groups of people talk about a controversial topic – but do not connect to people in the “other” group. Unified networks are formed by small to medium sized groups that are obscure or professional topics, conference hashtags are a good example. Fragmented networks have few connections among the people in them: these are often people talking about a brand or popular topic or event. Clusters sometimes grow among the people talking about a brand, indicating a existence of a brand “community”. Broadcast networks are formed when a prominent media person is widely repeated by many audience members, forming a hub-and-spoke pattern with the spokes pointed inward at the hub. The final pattern is the opposite, hub-and-spoke patterns with the hub linking out to a number of spokes. This pattern is generated by technical and customer support accounts like those for computer and airline companies. Additional patterns may exist, but these patterns are prominent in many social media network data sets.
When applied to external conversations, social media networks help identify the “mayor” of a hashtag or topic: these are the people at the center of the network. Network maps can be compared to the six basic types of networks to understand the nature of the topic community. We can look for examples of successful social media efforts and map those topic networks. Social media managers can contrast their topics with those of their aspirational targets and measure the difference between where they are and where they want to be.
When applied to enterprise conversations and connections, network analysis can reveal the experts who answer many people’s questions and “brokers” who bridge otherwise disconnected groups as well as the “structural holes” that show where a bridge or link is needed.
These insights can be useful in mergers, HR evaluation of group and manager performance, and identifying internal subject matter experts.
Research performed using NodeXL shows that work teams that have higher levels of internal connection (which is called “network density”) have higher levels of performance and profit. See:
The impact of intragroup social network topology on group performance: understanding intra-organizational knowledge transfer through a social capital framework
Wise, Sean Evan (2013) The impact of intragroup social network topology on group performance: understanding intra-organizational knowledge transfer through a social capital framework. PhD thesis, University of Glasgow.
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http://theses.gla.ac.uk/3793/