There will be a NodeXL social media network analysis training session at the Consumer Goods Sales and Marketing Summit in New York City on June 1, 2015.
Map
November 3 – 5, 2014 – Restaurant Executive Summit – Mapping restaurant networks in social media
The 2014 Restaurant Executive Summit will be held on November 3 – 5, 2014 at the Ritz-Carlton in Ft. Lauderdale, Florida.
The theme of the event is “How to Feed Consumers with a #Digital @ppetite”
I will speak about the ways that restaurants and dining experiences are discussed in social media. I will show network maps that visualize the relationships among people who talk about restaurants created with the free and open NodeXL social media network analysis and visualization application.
Here are some recent NodeXL social media network maps for mentions of major chain restaurants featured in the NodeXL Graph Gallery:
DunkinDonuts
McDonalds
Chipotle
@olivegarden
These maps illustrate the shape of the crowd that gathers around the names of major chain restaurants. A few Twitter user accounts occupy key positions in these network crowds, these are the influential voices that are repeated widely by others.
Closer inspection (click through for details) reveals smaller groups or clusters which form as a smaller set of people interact with one another more than with the larger population. These groups have distinct topics of interest which are summarized in the content report associated with each visualization.
The network and content report can reveal the topics of interest to various groups in the discussion as well as the key people within each group.
29-30 September at the University of Melbourne – Talk about social media networks
Best Practice in Data Journalism Workshop
PROGRAM
29-30 September 2014
Terrace Lounge, Level 1, Walter Boas Building, Parkville Campus
(Campus map at http://maps.unimelb.edu.au/parkville)
MONDAY 29 SEPTEMBER
| 9-9.30AM | REGISTRATION AND WELCOME |
| 9.30-9.45am | WELCOME AND INTRODUCTIONS- DR MARGARET SIMONS AND CARLTON CONNECT |
| 9.45am-11 | Presentations and Q and A from journalists: Marc Moncrieff and Craig Butt – Fairfax Media; Lisa Cornish – Red Cross (formerly News Corp); Harrison Polites – Business Spectator. |
| 11-11.30 | MORNING TEA |
| 11.30-12.30 | Presentations by Journalists (continued): Ed Tadros – Australian Financial Review; Matt Liddy, ABC; Nick Evershed – The Guardian in Australia. |
| 12.30-1PM | ROUNDTABLE DISCUSSION AND IDENTIFICATION OF COMMON THEMES AND CHALLENGES |
| 1PM-2PM | LUNCH |
| 2PM-2.30pm | AURIN – Exploring the potential – Presentation by Professor Richard Sinnott, University of Melbourne. |
| 2.30-3pm | NodeXL – Exploring the potential – Presentation by Marc Smith, Director, Social Media Research Foundation |
| 3-3.30pm | AFTERNOON TEA |
| 3.30PM-5PM | Panel Session – Big Data. What Next? With Craig Thomler (Delib), Professor Paul Jensen (Faculty of Business and Economics, University of Melbourne); Jodie McVernon, (School of Population and Global Health, University of Melbourne), Scott Ewing, (World Internet Project, Swinburne Institute for Social Research.) |
There will be a 3 hour session introducing NodeXL on Tuesday from 2-5pm 30th September at the main Parkville campus of UniMelb. The event is open to the public and is free.
It will be in the Old Arts Building Lecture Theatre B.
The main session will run from 2-4pm and there will be an additional hour for those that want to stop on for further training, finishing at 5pm
If you want to use NodeXL in the session, you will need a Windows laptop, and the Windows version of Excel (2007/2010/2013).
You can download NodeXL in advance from: http://nodexl.codeplex.com/.
Map and Building:
http://maps.unimelb.edu.au/parkville/building/149#.VCTinmS1Zlo
Download instructions:
VIDEO: SNA for fundraising and development webinar hosted by the Prospect Research Institute
I participated in a webinar hosted by the Prospect Research Institute. We discussed the ways that NodeXL can simplify the task of collecting social media and social network data. The tool generates easy to understand reports that highlight insights into connected structures.
The slides associated with the talk can be found here:
Analyzing Social Media Networks with NodeXL: Insights from a Connected World
May 15, 2014: Keynote at Sam and Irene Black School of Business at Penn State University
I will speak at the Sam and Irene Black School of Business at Penn State University on Thursday, May 15, 2014.
I will talk about the themes Thinking across Boundaries, Learning by Doing, and Innovating through Collaboration in the context of the work of the Social Media Research Foundation to deliver an end-user friendly, free and open tool for social media network analysis.
The NodeXL project from the Social Media Research Foundation has crossed many boundaries, notably bridging the divide between the social sciences and the computer sciences.
We have learned a great deal as the NodeXL development team has released hundreds of updates to the application, guided by the feedback of our growing user community.
The Social Media Research Foundation team has innovated at multiple levels: organizationally we are a modern, virtual, distributed group of collaborators. Technically, we have focused our project on ease of use and automation rather than scale and sophistication, our users are not programmers. We have implemented many innovative network analysis and visualization techniques because we have been driven by a need to serve a diverse user population. The contributors to the project are themselves from a diverse range of disciplinary backgrounds, making it easier to shape the tool for the broadest audience.
Which social media network type is your topic? Which did you want it to be?
There are at least six different types of social media network structures present in systems like Twitter and other services in which people are able to reply to one another.
Each of the six patterns is generated by the behavior of the individuals in the population.
In many cases the pattern you are is not the pattern you want to be.
This table describes each of the six patterns in terms of the difference between that pattern and the other five patterns.
Go down the rows until you find the pattern that most closely matches the network you currently have. Then work across the columns until you find the pattern that you want to become.
At the intersection is a color and a few ways to change and measure the transition from where you are to where you want to be.
A red square indicates an undesirable transition (who wants to become a divided discussion?). A yellow square is a low probability and difficult transition (it is hard to go from divided to unified). A blue square is a challenge but possible while a green square is a fairly easy transition to make.
Using this guide, you can plan a strategy for your social media engagement.
This slide is part of a larger slide deck about using social media network analysis to guide engagement. Look for slide 71.
Radio: OnTheMedia – Twitter Cartography – with Lee Rainie from Pew Internet
Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia this week about the recently released report on mapping Twitter topic networks. The report found six distinct patterns of social media networks in Twitter: divided, unified, fragmented, clustered, and in and out hub and spoke patterns. They discuss the prospects for overcoming polarization in social media and the hopeful signs that many other forms of social network structures exist in addition to the divided network pattern.
April 1, 2014 – NodeXL SNA of social media talk at Federal Big Data Working Group
I will present a talk about social media network at the April 1st Federal Big Data Working Group at 6:30pm.
Talk details are on the SemanticCommunity.info site.
The Federal Big Data Working Group supports the Federal Big Data Initiative and the Federal Digital Government Strategy.
See: http://www.meetup.com/Federal-Big-Data-Working-Group/
The talk will focus on the easy to follow steps needed to create social media network maps and reports automatically from services like Twitter, Facebook, YouTube, Flickr, email, blogs, wikis, and the WWW. Here is a sample network map of the term #bigdataprivacy:
The graph represents a network of 248 Twitter users whose recent tweets contained “#bigdataprivacy”, or who were replied to or mentioned in those tweets. The tweets in the network were tweeted over the 6-day, 10-hour, 29-minute period from Tuesday, 25 February 2014 at 14:36 UTC to Tuesday, 04 March 2014 at 01:06 UTC. 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 graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
@whitehouseostp, @mit, @mit_csail, @steve_lockstep, @aureliepols, @dbarthjones, @digiphile, @stannenb, @djweitzner, @mikaelf
Top URLs in Tweet in Entire Graph:
http://web.mit.edu/bigdata-priv/webcast.html
http://www.commerce.gov/news/secretary-speeches/2014/03/03/us-secretary-commerce-penny-pritzker-delivers-remarks-mit
http://web.mit.edu/bigdata-priv/agenda.html
http://www.whitehouse.gov/blog/2014/02/24/privacy-workshop-explore-big-data-opportunities-challenges
http://www.nytimes.com/glogin?mobile=1&URI=http%3A%2F%2Fmobile.nytimes.com%2F2014%2F03%2F03%2Ftechnology%2Fwhen-start-ups-dont-lock-the-doors.html
http://www.techrepublic.com/article/privacy-concerns-about-data-collection-may-lead-to-dumbing-down-smart-devices/
http://m.technologyreview.com/news/525131/intel-designs-a-safe-meeting-place-for-private-data/
http://thedatamap.org
http://www.foreignaffairs.com/articles/140741/craig-mundie/privacy-pragmatism
http://www.cs.ucdavis.edu/~franklin/ecs289/2010/dwork_2008.pdf
Social media network maps and reports covered in the press
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
October 24th, 2013: NodeXL Social Media Network Analysis Workshop at Arizona State University
I will speak about social media networks on October 24th, 2013 at the department of Computer Science at the Arizona State University.
The graph represents a network of 712 Twitter users whose recent tweets contained “@ASU”, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Sunday, 13 October 2013 at 19:56 UTC.
The tweets in the network were tweeted over the 4-day, 21-hour, 47-minute period from Tuesday, 08 October 2013 at 21:48 UTC to Sunday, 13 October 2013 at 19:35 UTC.
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 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 Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values