The graph represents a network of 4,405 Twitter users whose tweets in the requested range contained “#pdf15 OR #wegov OR pdmteam OR @techpresident OR “personal democracy” OR Mlsif”, tweeted over the 42-day, 2-hour, 38-minute period from Saturday, 02 May 2015 at 21:24 UTC to Sunday, 14 June 2015 at 00:02 UTC.
Top 10 Vertices, Ranked by Betweenness Centrality:
Top Hashtags in Tweet in Entire Graph:
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
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.
A social network consisting of Twitter users (nodes) who have Tweeted the word “global warming” connected to one another based on Follow, Reply, or Mention relationships (edges). Nodes are assigned different colors based on clusters. Hubs with many followers are indicated by size.
The chapter outline:
A Brief History of Social Network Analysis
Social Network Analysis and Human-Computer Interaction
Goals of Social Network Analysis for HCI Researchers and Practitioners
1) Inform the design and implementation of new CSCW systems
2) Understand and improve current CSCW systems
3) Evaluate impact of CSCW system on social relationships
4) Design novel CSCW systems and features using SNA methods
5) Answer fundamental social science questions
Social Network Analysis Questions
Questions about Individual Social Actors
Questions about Overall Network Structure
Questions about Network Dynamics and Flows
Performing Social Network Analysis
Identify Goals & Research Questions
Sources of Network Data
Types of Social Networks
Representing Network Data
Three ways of representing network data
How to Analyze and Visualize Data
Network Analysis Tools
Commonly Used Network Analysis and Visualization Tools
Node-Specific Metrics: Focusing on the Trees
Common Centrality Metrics
Aggregate Network Metrics: Focusing on the Forest
Common Aggregate Network Metrics
Network Clusters & Motifs: Focusing on the Thickets
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:
This is a highly fragmented “Brand” network pattern with several prominent Broadcast hub and spoke structures centered around the most central participants: @thenextweb, @ow, @epro, @nicolasfordham, @gcouros, @malchord, @martinsfp, @plagia3, @k5launch, @taxion2.
I spoke about how anyone who can make a pie chart can now make these network maps and reports.
I will be running hands-on workshops on Tuesday working with NodeXL to map company and market topics. Wednesday is a basic overview of networks, social networks, social media networks. I will talk about the value of networks for businesses. The network perspective makes it easy to recognize that the same number of people can organize themselves into a variety of connected structures and that some structures are better than others for various purposes.
An example is the map of the connections among the people who tweeted about Consortium member Sage North America.
Networks are everywhere but collecting, analyzing, visualizing, and gaining insights into connected structures can require advanced technical skills. This session presents a free, easy-to-use tool for network analysis that builds on the familiar Excel spreadsheet called NodeXL. If you can make a pie chart, you can get insights into networks. The tool makes it easy to collect data from a range of social media (Twitter, Facebook, YouTube, etc.). Quickly create visualizations and reports on the shape of connected groups. Identify the key people, groups and topics in a community. Network analysis can reveal the hidden structures in streams of interactions.