Unleashing Twitter Data for Fun and Insight

Location: Mission City B4
Average rating: ***..
(3.50, 6 ratings)

This talk demonstrates how an eclectic blend of storage, analysis, and visualization techniques can be used to gain a lot of serious insight from Twitter data, but also to answer fun quesions such as “What does Justin Bieber and the Tea Party have (and not have) in common?”

  • Overview – Why you should or shouldn’t stick around for the rest of the talk
  • Trends, Tweets, and Retweet Visualizations – A very quick overview of how to get your wheels spinning with Twitter data. Getting it. Analyzing it. Visualizing it.
  • Friends, Followers, and Setwise Operations – A look at some of the things you can compute with friendship data and setwise operations, making the case for Redis as an data store for this type of analysis, and presenting techniques for analyzing potential influence and interests of Twitterers. This block wraps up with a look at what you can learn by computing the largest friendship clique in someone’s network (and how you might stand to gain from it.)
  • The Tweet, the Whole Tweet, and Nothing but the Tweet – Focuses in on tweet data (as opposed to friendship data), and presents techniques for answering questions such as how often particular Twitters are mentioning one another, whether or not users are spammy with their hashtags, and who’s getting retweeted the most often (and what this might say about trust or influence.) This block makes the case for CouchDB as an ideal data store for this type of analysis, and wraps up by juxtaposing and visualizing tweet data for #JustinBieber and #TeaParty to answer the question “What does Justin Bieber and the Tea Party have (and not have) in common?”
Photo of Matthew Russell

Matthew Russell

Digital Reasoning Systems

Matthew Russell is a computer scientist and author with deep expertise in open source, data mining, and web application technologies. As tangible evidence, he has authored the O’Reilly titles Mining the Social Web and Dojo: The Definitive Guide. Matthew has worked extensively with the government as well as the private sector in and out of uniform to ensure that complex software is delivered on time and under budget in the midst of even the most difficult of circumstances. Matthew can help you implement elegant end-to-end solutions to difficult problems involving spartan resources, nebulous requirements, high pressure, and messy data.


  • Thomson Reuters
  • EMC Data Computing Division
  • EnterpriseDB
  • Microsoft
  • Gnip
  • Rackspace Hosting
  • IBM
  • Windows Azure MarketPlace DataMarket
  • Amazon Mechanical Turk
  • Amazon Web Services
  • Aster Data
  • Cloudera
  • Clustrix
  • DataStax, Inc. (formerly Riptano, Inc.)
  • Digital Reasoning Systems
  • Heritage Provider Network
  • Impetus
  • Jaspersoft
  • Karmasphere
  • LinkedIn
  • MarkLogic
  • Pentaho
  • Pervasive
  • Revolution Analytics
  • Splunk
  • Urban Mapping
  • Wolfram|Alpha
  • Esri
  • ParAccel
  • Tableau Software

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