We could not be happier about the initial response to the beta test of “Sifter” (http://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: http://sifter.texifter.com/Home/Pricing.
It’s official. Starting in January 2013, DiscoverText customers will be able to purchase monthly access to four vibrant Gnip-enabled Power Track data feeds. Building on current successes with Twitter, we are pleased to offer unprecedented federated Power Track access to WordPress, Disqus, and Tumblr as part of our social #bigdata offering. Keep an eye on the blog for the launch in early January.
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.
Texifter was the first company to join as a paying customer in the alpha “Snapshot” offering from Gnip. You can still take part in that alpha by submitting a request for a free estimate of a snapshot from Twitter’s complete history.
This is, however, a very fast-moving landscape for for social #bigdata. We are quickly transitioning from the alpha “Snaphot” tests to the beta of a cradle-to-grave system for building estimates for the cost of text analytic projects that feature either the real-time day-forward, Gnip-enabled Power Track (the Twitter fire hose), or the new historical Power Track. So if you have ever wished you could go back in time and collect all the tweets from an epic moment in history, your wish just came true. Contact us if you have any questions and submit a request for a free estimate today.
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.
The use of social media has grown exponentially over the last several years. In fact, most television programs and televised advertising have a social media component, designed to expand reach and engagement with the audience. To date, the tobacco control community has relied on traditional media—paid television, radio, billboard and print media advertising—to promote their messages.
On March 19, 2012, the Centers for Disease Control and Prevention (CDC) launched Tips from Former Smokers. This campaign was the CDC’s largest anti-smoking campaign ever and its first national advertising effort. The campaign will last four months and consist of both traditional and social media. The Health Media Collaboratory at the University of Illinois at Chicago, directed by Sherry Emery, PhD, will measure and evaluate a key social media component of the campaign—its Twitter reach and impact.
Using DiscoverText with GNIP’s Power Track provides full access to Twitter’s Firehose. This is in contrast to Twitter’s publicly available API stream, which provides only a 1% sample of tweets. Because the volume of tweets for health social media campaigns are relatively low, every tweet matters. Access to GNIP’s premium Twitter feed allows us to capture all tweets and metadata for the campaign.
The use of DiscoverText to sift through tweets and code for content provides a useful tool for measuring online public engagement, audience sentiment, and campaign discourse.
The Collaboratory will report on the overall reach and audience engagement of the campaign through an analysis of unique users reached, number of retweets, and mentions. This information will not only track the engagement of individual users but also measure the engagement of state tobacco control programs in the campaign. A sentiment analysis will be conducted on tweets to gauge the emotional valence of the campaign and individual television ads. Finally, using root keywords for quitting and smoking uptake, the numbers of twitter users that express interest in quitting or prevention will be reported.
The sign up remains open. Jump in and let us know if you like our Enterprise solution for social media analytics.
Texifter is launching a second beta test period using “Power Track for Twitter” fire hose filtering a service provided by GNIP. We have streamlined the process of providing Enterprise class access to the beta test. This beta includes access to an expanding set of tools for archiving, filtering, coding, validating and machine classifying text. You can train a custom machine classifier in about 30 minutes.
The GNIP Power Track, in partnership with Twitter, provides users with unrestricted, real-time filtering of the Twitter fire hose. This enriched feature for DiscoverText provides a valuable analytical tool to our users. Not only will the GNIP Power Track provide users with access to the full stream of fire hose data, it will also provide Klout scores, language data, re-tweet frequency, geographic coordinates, and all #hashtags where available in the results. Taken together, this quantity of data and rich metadata fields will allow users to perform valuable social media analysis within DiscoverText.
For more information: info@DiscoverText.com
On November 9th, the Federal Emergency Management Agency(FEMA) conducted its first national test of the Emergency Alert System. In some communities this meant full involvement, with teams responding to mock emergencies, and managers monitoring the execution. In the deaf community, the response to monitor was regarding two Twitter hashtags, #SMEM, and #DEMX. The #SMEM hashtag is specific to the emergency response community, and was created over a year ago, and the #DEMX hastag is specific to the deaf community, but created specifically for this event. Monitoring the usage of these hashtags was Steph Jo Kent, a PhD. Candidate in Communications at the University of Massachusetts. Steph’s goal was to monitor the spread of these hashtags throughout the deaf community and emergency response community and how they crossed channels. In order to do this, she utilized DiscoverText, which is how I was lucky enough to become involved in the project.
Monitoring these specific Tweets adds to the already diverse functionality of DiscoverText. To start the project, we simply used the Twitter API to harvest uses of #SMEM and #DEMX beginning on November 2. After the event on November 9, we continued to harvest uses of the hashtags. By early December, we had archived nearly 800 Tweets using the hashtag #DEMX, and nearly 8,000 Tweets using the hashtag #SMEM. From these two archives, it is possible to breakdown Tweets by time and person, giving us valuable information about key individuals and how they spread the hashtag. For Steph’s research, it was particularly valuable to isolate the crossover between the two hashtags. Using our search feature, we were able to isolate cases of crossover and bucket those results. This allows us to move from noisy data, to a more manageable and germane grouping of Tweets.
From here, we utilized the newly optimized TopMeta feature to breakdown the occurrences by day and by user. We were able to discover which days and individuals produced the most Tweets. The information we found allowed us to better visualize how the Tweets broke down before and after the event. The results showed a small number of users producing the majority of Tweets, and that prior to the event, there was more usgage of the hashtags. Unfortunately, the mass crossover of Tweets that we had envisioned did not occur. There was a minimal amount of crossover, meaning the message did not travel well through the two communities. Steph has posted a detailed analysis of her findings on her blog, where she uses her expertise to analyze the project.
In the future, this same methodology can be applied to hashtags that have been created for marketing or other purposes, such as hashtags for television shows and large events. There is valuable information in these hashtags; they reflect an emergent folksonomy that influences how ideas, links and memes spread over Twitter.
Using the GNIP Power Track, these hashtags can be leveraged as metadata, broken down over time and used to display how well information did or did not travel. Overall, this was great experiment, and I am happy to have had the opportunity to collaborate with Steph, and to have participated in a project that has the power to influence the way social media is used to interact those in the deaf community.