See on Scoop.it – Social Network Analysis – Critique

The way information spreads through society has been the focus of intense study in recent years. This work has thrown up…

See on medium.com

See on Scoop.it – Social Network Analysis – Critique

“This is a summary and analysis of Pierre Lévy‘s book “Collective Intelligence: mankind’s emerging world in cyberspace”, which provides all direct and indirect quotes in this essay…”

See on www.mindforms.net

See on Scoop.it – Social Network Analysis – Critique

See on snowdenandthefuture.info

I am placing this page here for my own future record and use… The original is available here.

Mining the Social Web, 2nd Edition – All Numbered Examples

This page provides a convenient catalog of all numbered examples that are included in the IPython Notebooks as part of this GitHub repository. Be sure to visit http://MiningTheSocialWeb.com for more updates, example code, and long-form content that didn’t make it into the book…

Chapter 1 – Mining Twitter

• Example 1. Authorizing an application to access Twitter account data
• Example 2. Retrieving trends
• Example 3. Displaying API responses as pretty-printed JSON
• Example 4. Computing the intersection of two sets of trends
• Example 5. Collecting search results
• Example 6. Extracting text, screen names, and hashtags from tweets
• Example 7. Creating a basic frequency distribution from the words in tweets
• Example 8. Using prettytable to display tuples in a nice tabular format
• Example 9. Calculating lexical diversity for tweets
• Example 10. Finding the most popular retweets
• Example 11. Looking up users who have retweeted a status
• Example 12. Plotting frequencies of words
• Example 13. Generating histograms of words, screen names, and hashtags
• Example 14. Generating a histogram of retweet counts
  

  Chapter 2 – Mining Facebook

• Example 1. Making Graph API requests over HTTP
• Example 2. Querying the Graph API with Python
• Example 3. Results for a Graph API query for Mining the Social Web
• Example 4. Querying the Graph API for Open Graph objects by their URLs
• Example 5. Comparing likes between Coke and Pepsi fan pages
• Example 6. Querying a page for its “feed” and “links” connections
• Example 7. Querying for all of your friends’ likes
• Example 8. Calculating the most popular likes among your friends
• Example 9. Calculating the most popular categories for likes among your friends
• Example 10. Calculating the number of likes for each friend and sorting by frequency
• Example 11. Finding common likes between an ego and its friendships in a social network
• Example 12. Calculating the friends most similar to an ego in a social network
• Example 13. Constructing a graph of mutual friendships
• Example 14. Finding and analyzing cliques in a graph of mutual friendships
• Example 15. Serializing a NetworkX graph to a file for consumption by D3
• Example 16. Visualizing a mutual friendship graph with D3
  

• page continues here

Allow time to load and best viewed with mozilla firefox.

The Twitter conversation leading up to the G8 meeting of 2013 held in N. Ireland.  It contains 38,762 nodes or Twitter accounts and hashtags with 116,737 edges or connections between them. The network is directed and was collected from Fri, 14 Jun 2013 09:13. The g8 summit took place on the 17-18 June 2013.