There will be a one day crash course on all things “big data” at the upcoming San Francisco Predictive Analytics World conference on Monday, March 30th, 2015. Get the Big Data big picture with a day of introduction to the major concepts, methods, challenges, and best practices related to leveraging large volumes of information.
There will be a session on social media network analysis using NodeXL at the conference as well.
Networks are collections of connections — they are everywhere once you start to look. Learn how to collect, analyze, visualize, and publish insights into connected populations. Using the free and open NodeXL addin for Excel, anyone who can make a pie chart can now make a network chart. Create insights into social media, collaboration, organizations, markets, and other connected structures with just a few clicks. Easily publish reports with visualizations and content analysis. Apply social network analysis to your own brands, email, discussions or web sites.
I am delighted to return to South Africa where I will participate in the Mammoth BI conference in Cape Town, on November 17-18, 2014 at the Cape Town International Conference Centre, Convention Square, 1 Lower Long Street, Cape Town, 8001, Western Cape, South Africa.
I will speak at the University of Nebraska-Lincoln (UNL) at a symposium on The Future of Big Data in Lincoln, Nebraska, on November 6 and 7, 2014.
The event will feature presentations from academia, government, and the private sector, and workshops/lectures on topics related to big data. This event is open to the public.
Students and postdoctoral researchers are welcome to attend. The event should bring together people working in the computational sciences, federal agencies, and industry experts specializing in data management, analytics, and the future of information.
8:45 a.m. Tim Hesterberg, Google 9:30 a.m. Valinda Scarbro Kennedy, IBM Academic Initiative, Relationship Manager 10:15 a.m. Break 10:45 a.m. Jeffrey Gerard, The Climate Corporation 11:30 a.m. Jerry Roell, John Deere 12:15 p.m. Lunch; Tsengdar Lee, Project Manager, NASA 1:30 p.m. Two Concurrent Sessions:
Ag & Natural Resources
1:30 p.m. Adina Howe, Argonne National Lab Soil Microbiome 2:15 p.m. Natalia De Leon, Wisconsin 3:00 p.m. Heuermann Reception Lecture on Future of Agriculture 3:30 p.m. Heuermann Lecture on Future of Agriculture
1:30 p.m. Carl Lundstedt, UNL/CERN 2:15 p.m. Heidi Imker, Ullinois (Libraries) 3:00 p.m. Break 3:30 p.m. Marc Smith, Social Media Research Foundation
5:00–7:00 p.m. Poster Session and Reception
Friday, November 7
8:30 a.m. Adam Glynn, Emory University, and Konstantin Kashin, Harvard; Big Data and Social Sciences 9:15 a.m. Jennifer Thoegersen, UNL Data Curation Librarian 10:00 a.m. Panel with representatives from federal agencies to discuss funding opportunities:
Philip E. Bourne, Ph.D., Associate Director for Data Science, NIH
Ian Foster, Ph.D., Director of the Computation Institute & Argonne Distinguished Fellow, Argonne National Lab
George Strawn (Director, Networking and Information Technology Research & Development; NITRD)
12:00 p.m. Lunch and Keynote Speaker (Animal Sciences)
1:00 p.m. Todd Mockler, Danforth Center 1:45 p.m. Henry Neeman, HPC, University of Oklahoma 2:30 p.m. Adjourn
There are at least three phases of possible success for a social media marketing effort: phase 1, you get an audience of people who will retweet what you post. Phase 2, some of your audience gets its own audience for the content they repost from you. Phase 3, a dense web of relationships emerges, a community of relationships. This is a desirable phase because it sustains the conversation event when new messages from the brand account are not created.
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.
lennyism OR insightnovation OR #IIeX Twitter NodeXL SNA Map and Report for Monday, 09 June 2014
The graph represents a network of 611 Twitter users whose tweets in the requested date range contained “lennyism OR insightnovation OR #IIeX”, or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 09 June 2014 at 00:24 UTC.
The requested date range was from Tuesday, 01 April 2014 at 00:00 UTC through Sunday, 08 June 2014 at 23:59 UTC.
The tweets in the network were tweeted over the 67-day, 4-hour, 35-minute period from Tuesday, 01 April 2014 at 00:26 UTC to Saturday, 07 June 2014 at 05:01 UTC.
There is an edge for each “replies-to” relationship in a tweet, an edge for each “mentions” relationship in a tweet, and 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.
Top 10 Vertices, Ranked by Betweenness Centrality: