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 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:
The talk will focus on free and open tools for creating network maps and reports that can illuminate the landscape of social media.
The graph represents a network of 633 Twitter users whose tweets in the requested date range contained “sqlpass”, or who were replied to or mentioned in those tweets. The tweets in the network were tweeted over the 15-day, 2-hour, 48-minute period from Tuesday, 25 February 2014 at 00:26 UTC to Wednesday, 12 March 2014 at 03:15 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’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:
@sqlpass
@laertesqldba
@nikoneugebauer
@karlakay22
@jenstirrup
@retracement
@grrl_geek
@sqlrockstar
@markvsql
@sqlservermag
The SBP conference provides a forum for researchers and practitioners from academia, industry, and government agencies to exchange ideas on current challenges in social computing, behavioral modeling and prediction, and on state-of-the-art methods and best practices being adopted to tackle these challenges. Interactive events at the conference are designed to promote cross-disciplinary contact.
Social Computing harnesses the power of computational methods to study social behavior within a social context. Behavioral Cultural modeling refers to representing behavior and culture in the abstract, and is a convenient and powerful way to conduct virtual experiments and scenario analysis. Both social computing and behavioral cultural modeling are techniques designed to achieve a better understanding of complex behaviors, patterns, and associated outcomes of interest. Moreover, these approaches are inherently interdisciplinary; subsystems and system components exist at multiple levels of analysis (i.e., “cells to societies”) and across multiple disciplines, from engineering and the computational sciences to the social and health sciences.
Data Science
Ballroom ABNetworks are everywhere, particularly in social media. Understanding networks can quickly reveal the key people, groups, and topics that matter most. But the tools to collect, analyze, visualize, and gain insights into connected structures have remained complex. Now the free and open NodeXL application makes network analysis tasks as easy as making a pie chart. The Network Overview Discovery and Exploration add-in for Excel (2007, 2010, 2013) extends the familiar spreadsheet, enabling users to easily access networks from a range of data sources including Facebook, YouTube, Twitter, Flickr, email, message boards, Wikis, blogs, and other repositories of connections. With simple automation tools, NodeXL users can calculate a range of network metrics, create a visualization, and generate a report highlighting key people, groups, and top URLs, hashtags, words and word pairs used in the discussion network. Network maps have revealed many of the hidden structures of social media, defining the major differences in the shapes and structures created as people link to one another.
If you have questions on social network analysis, meet with Marc to talk about:
NodeXL and related network analysis and visualization tools
How to collect, store, analyze, visualize, summarize and publish social network reports with just a few clicks (and no coding)
How to identify key influential people and subgroups within a conversation network
How to apply social network analysis to social media marketing
How to apply organizational network analysis to enterprise collaboration
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:
@strataconf
@oreillymedia
@thedatacrunch
@dataiku
@alpinedatalabs
@sasanalytics
@zettaforce
@bigdata
@josemariasiota
@tibco
You can make these types of maps with just a few clicks using NodeXL.
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.
