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:
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I will be speaking at the University of Sydney on September 26th, 2014 as part of the 2014 Sydney Disrupt conference.
This year’s theme is “ https://www.videoschoolonline.com/?doc=insider-trading-essay Living Disruption”.
Speakers include:
Here is a recent map of the connections among people who tweeted “#disrupt“:
Here is a video review of the 2013 #Disrupt event.
I will also lead a workshop on using NodeXL, in a hands-on training on 25th September 2014 from 9am to 12pm in the Link Building (J13), Room 417.
The workshop is sponsored by the Digital Disruption Research Group (DDRG), in collaboration with the Complex Systems Research Group at the Faculty of Engineering & IT.
RIS News, the leading media organization for retail executives, and Consumer Goods Technology, the leading source for the consumer goods sector, have partnered to launch the 2014 Retail and Consumer Goods Analytics Summit. The conference hashtag is #RCAS2014.
I will speak on September 12th at the event. I present the Closing Keynote from 2:30 – 3:10pm on Friday. My talk is titled:
The Impact of Social Media on Data & Analytics
Speaker: Marc Smith, Director, Social Media Research Foundation
People talk about the products and services the use, love or hate all the time in social media. These conversations can be better understood through perspective of social network analysis. Network theory views the world as a web of connected people. Network analysis builds measures and visualizations of collections of connections to reveal the key people, groups and issues in these conversations. Using social media network maps and reports the confusing landscape of tweets and posts comes into focus. Information visualizations of the virtual crowds of people gathered around every brand, product, event, or service highlights the range of variation in the shape of these crowds. Six different patterns have been identified so far, allowing social media managers to recognize the nature of the brand network they have and the nature of the network they want to have. Network measures are useful as KPIs for tracking not just the size and volume of a social media stream, but also the shape and structure of the pattern of connections. The six patterns: divided, unified, fragmented, clustered, and in and out hub and spoke, are a useful guide to strategic engagement in social media.
risnewsinsights Twitter…
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consumergoods OR cgsm20
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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.
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I will speak this December 12-14 , 2013 at the DISC2013 International Conference on Social Network Analsysis at Yeungnam University in Daegu, South Korea – http://bit.ly/17LtWlo
My host is conference organizer Dr. Han Woo Park, director or the Webometrics Lab at Yeungnam University
After the conference I will travel to visit with other research groups in Korea focused on the study of social media. I will be traveling with a colleague, Dr. Jana Diesner, professor of information science at the University of Illinois at Urbana-Champaign.
Schedule for December:
12-14 Asia Triple Helix Forum
16 Workshop at POSTECH with Dr. Woo-Sung Jung
17 Talk at KAIST with Dr. Il-Chul Moon
18 Workshop at Treum, Meeting with Dr. Gihong Yi
19 Sogang University and KISTEP
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I will participate in a workshop at IREX in Washington D.C. on November 13, 2013.
The workshop is titled Social Network Analysis: Influence and Impact Beyond Likes and Retweets. We will focus on the applications of social network analysis for development efforts, exploring how SNA can:
The workshop will be facilitated by Wayan Vota along with three social network analysis researchers:
This Deep Dive will be an active event. We will mix thoughtful discussions with experiential activities, building social capital while we learn about social networks. Participants are encouraged to submit social media topics in advance so maps and reports can be generated for the event.
Note that this event is in-person only, so please RSVP now to attend.
How Social Network Analysis Can Improve Impact
IREX Tech Deep Dive
8:30am-12:30pm,
November 13th, 2013
IREX Headquarters
Washington, DC
Newly updated slides are now available that provide an overview of the NodeXL project from the Social Media Research Foundation:
Updates to NodeXL are on the way to expand the variety of data importers, improve web publication, and scale to large data sets.
Here is a round up of the social media network analysis talks, workshops, and training events I will be doing in the coming months:
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June 3-7, 2013 Social Media Network Analysis Workshop at LINKS2013 University of Kentucky |
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May 20-24, 2013
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April 15th, 2013
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April 12, 2013 PASS Business Analytics Conference: Mapping social media with NodeXL Chicago, IL. |
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April 8 and 9, 2013 NSF workshop: Kredible.Net – Study reputation and social roles on social mediaPurdue University |
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March 26, 2013 OCTribe – San Francisco Online Community Meetup – Mapping Social Media with NodeXL San Francisco, CA |
This April 8 and 9, 2013 an NSF funded workshop called Kredible.Net to be held at Purdue University will bring together researchers studying reputation and social roles in social media.
The grant will help researchers investigate how social media, especially Wikipedia articles and editors, shape public knowledge. The project aims to build a research community and to propose a research agenda for the study of reputation and authority in informal knowledge markets, such as Wikipedia.
The team is lead by Sorin Adam Matei (PI) of Brian Lamb School of Communication, Elisa Bertino (Cyber Center), Michael Zhu and Chuanhai Liu (Department of Statistics) , and Luo Si (Department of Computer Science).
The KredibleNet project is supported by an international team of researchers including Marc Smith, director of the Social Media Research Foundation and the NodeXL project and by Dr. Luca De Alfaro, from University of California, Santa Cruz, the head of the WikiTrust project. The team also includes researchers Jure Leskovec (Stanford), Bernie Hogan (Oxford Internet Institute), and Ben Shneiderman (University of Maryland).
The KredibleNet grant will fund research and workshops that will reveal how social media leaders gain authority to shape public knowledge.
See: http://kredible.net/in/kredible-net-awarded-nsf-grant-to-study-reputation-and-social-roles-on-social-media/
The video from my talk is on YouTube:
Just released: Science@Microsoft – The Fourth Paradigm in Practice Book
“This collection of Science@Microsoft vignettes illustrates some of the progress that has been made in a number of disciplines and describes the technologies that have been deployed to gain these new insights.”
The volume lists tools for scientific research and includes NodeXL:
NodeXL is a powerful and easy-to-use interactive network visualization and analysis tool that uses Microsoft Excel for representing generic graph data, performing advanced network analysis, and visual exploration of networks. NodeXL supports multiple social network data providers that import graph data (nodes and edge lists) into Excel. The import features of NodeXL explore social media by pulling data from personal email indexes on the desktop, Twitter, Flicker, YouTube, Facebook, and web hyperlinks.
NodeXL allows non-programmers to generate useful network statistics and metrics quickly and create visualizations of network graphs. Filtering and display attributes can be used to highlight important structures in the network.
