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:
At the Hospitality Technology’s Hotel Technology Forumin Puerto Rico. April 22, 2015 there will be a talk about applying social media network analysis to better understand how guests talk about hotels in social media.
The guest experience is often reflected in the social media they create while on the property and after.
Some social media can help elevate a brand and provide insights and new ways to deliver services.
Some social media can amplify service problems and erode a brand.
Social media network maps and reports can help businesses navigate the complex landscape of social media, identifying the key people, groups and topics in the discussions that are relevant to the business.
In this talk we will review the ways NodeXL can be used to generate maps and reports that can guide a social media engagement strategy.
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 will speak about the value of a network perspective for the discovery of fraud and corruption in financial data at the December 9th session of the World Bank’s upcoming meeting of the Stolen Asset Recovery Initiative.
“The World Bank Group’s International Corruption Hunters Alliance (ICHA) brings together heads and senior officials of corruption investigating bodies and prosecuting authorities, anti-corruption experts, academics, and representatives of international organizations from over 130 countries. The 2014 meeting of the Alliance will focus on fighting corruption – and the vast illicit outflows generated by corruption – by sharing know-how and experiences in the use of both traditional and alternative corruption fighting approaches.”
All financial transactions create a network as one person transfers money from one account to another. A list of transactions creates a web of connections with an emergent shape or pattern. Within these patterns are key positions occupied by people with special power in the network. Mapping these transaction networks can reveal the hidden traces of financial crime.
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.
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: