We could not be happier about the initial response to the beta test of “Sifter” (https://sifter.texifter.com), a self-serve tool to get free estimates of the cost to pull samples from the complete (un-deleted) history of Twitter. Using the powerful Gnip-enabled Power Track operators, we have a few hundred early adopters testing out rules that allow them to pull highly selective samples going back to the very first day of Twitter. For information on pricing to license the Twitter data, please visit: https://sifter.texifter.com/Home/Pricing.
It was a great joy to return to the University of Amsterdam and give this talk to my old friend Richard Rogers and his 100+ attentive workshop attendees.
Just about six hours left to win valuable historical twitter datasets and powerful text analytics software. This is by far our best Facebook raffle yet. To enter:
- Login to Facebook
- Visit this URL: https://bit.ly/1421tWP
- Tweet about the raffle, follow DiscoverText on Twitter, or like on Facebook.
- Do all three to increase your chances.
- Refer friends to do better still.
The winner will get three 10-day historical Twitter datasets, with Power Track search operators enable by our friends @gnip as well as gratis use of the DiscoverText software platform. Runners up will also get valuable software prizes for a full year.
We interviewed researchers at the University of Illinois Chicago in the Health Media Collaboratory about their use of DiscoverText and the Gnip-enabled Power Track for Twitter to study smoking behavior. The team, led by Dr. Sherry Emery, explains why it is important to train and use custom machine classifiers to sort the millions of tweets they are collecting from the full Twitter fire hose. The UIC team strongly argues for the combination of good tools and highly reliable data.
Just in time for the 2012 GOP convention, we are running a special offer to provide full Twitter fire hose access via the Gnip-enabled Power Track for Twitter: Never miss a tweet. Full coverage with no rate limits. Powerful search rules, text analytics, clustering and machine-learning via custom machine classifiers.