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Gnip’s asset and investment management clients are consistently impressed by two aspects of our social media data that differentiate this data from their other sources: Speed & Amplification.

Speed

Speed relates to the ability of social media content to be ‘instant’; an ability fueled by millions of global users who can break news and sentiment more immediately than traditional media sources always can.

A prime example is news of the death of Osama Bin Laden. Keith Urbahn, the former chief of staff for Don Rumsefeld, is widely credited with the breaking that story… through Twitter!

After Keith’s tweet, multiple retweets quickly followed. Within 19 tweets on this subject, a company called DataMinr had identified this as an important and breaking story. DataMinr, a “global sensor network for emerging events and consumer signals,” then issued a signal to their clients, alerting them to this important piece of information.

How does this play into the ‘speed’ characteristic? Because it would be over 20 minutes before that story appeared on traditional news sites. Access to a data stream that can beat traditional media sources by over 20 minutes requires no explanation as to its value for traders and investors.

Amplification

Amplification speaks to the ability of social media as a ‘crowd-sourced megaphone.’ The propensity of users to like, share, and retweet content from other users gives those consuming social media data an extremely easy mechanism to measure what content is most important to the world – and compare that content against other content in real time.

A prime example is the passing of Steve Jobs. We wrote about Steve Jobs’ passing a few weeks ago – that post is here – but there’s an important item to revisit:

The impact he had on us made his death that much more profound and the reaction on Twitter was immediate and immense. Word spread rapidly, peaking at 50,000 Tweets per minute within 30 minutes. At that point, Tweets about Jobs accounted for almost 25% of all Tweets being sent globally.

Access to Gnip’s social media data stream allowed our clients to measure, in the moment, the amplification of this story to measure the importance the world placed on this piece of news. While I doubt any of us needed to see those numbers to know Steve’s passing was an important piece of news, that’s a clear example of how ‘amplification’ works.

Our clients use amplification as a measure to weigh the importance of breaking news, upcoming events, market and product announcements, etc. against other stories. By capturing a realtime snapshot of what the market considers important – and what it doesn’t – they’re able to add an important factor to their existing algorithms.

None of this is to suggest that either social media data speed or amplification should be a sole factor in investing. But when the Gnip social media data stream provides clients with an additional factor to help understand or predict market fluctuations, the value is obvious.

Customer Spotlight – Klout

August 9th, 2011

Providing Klout Scores, a measurement of a user’s overall online influence, for every individual in the exponentially ever-growing base of Twitter users was the task at hand for Matthew Thomson, VP of Platform at Klout. With massive amounts of data flowing in by the second, Thomson and Klout’s scientists and engineers needed a fast and reliable solution for processing, filtering, and eliminating data from the Twitter Firehose that was unnecessary for calculating and assigning Twitter users’ Klout Scores

“Not only has Gnip helped us triple our API volume in less than one month but they provided us with a trusted social media data delivery platform necessary for efficiently scaling our offerings and keeping up with the ever-increasing volume of Twitter users.”

- Matthew Thomson
VP of Platform, Klout

By selecting Gnip as their trusted premium Twitter data delivery partner, Klout tripled their API volume and increased their ability to provide influence scores by 50 percent among Twitter users in less than one month.

Get the full detail, read the success story here.

 
Like many startups seeking to enter and capitalize on the rising social media marketplace, timing is everything. MutualMind was no exception: getting their enterprise social media management product to market in a timely manner was crucial to the success of their business. MutualMind provides an enterprise social media intelligence and management system that monitors, analyzes, and promotes brands on social networks and helps increase social media ROI. The platform enables customers to listen to discussion on the social web, gauge sentiment, track competitors, identify and engage with influencers, and use resulting insights to improve their overall brand strategy.

“Through their social media API, Gnip helped us push our product to market six months ahead of schedule, enabling us to capitalize on the social media intelligence space. This allowed MutualMind to focus on the core value it adds by providing advanced analytics, seamless engagement, and enterprise-grade social management capabilities.”

- Babar Bhatti
CEO, MutualMind

By selecting Gnip as their data delivery partner, MutualMind was able to get their product to market six months ahead of schedule. Today, MutualMind processes tens of millions of data activities per month using multiple sources from Gnip including premium Twitter data, YouTube, Flickr, and more.
 
Get the full detail, read the success story here.

Over 500 individuals recently gathered in New York City for this year’s TechCrunch Disrupt Hackathon. This annual event, fueled by pizza, beer, and Red Bull, features teams of die-hard techies that spend 20 hours, many without sleep (hence the Red Bull), developing and coding the next big idea. Participants compete in a lightning round of pitches in front of a panel of judges with the winners receiving an opportunity to pitch on the main stage at the TechCrunch Disrupt Conference in front of more than 1,000 venture capitalists and industry insiders.

We are excited that one of the apps that was developed at the 2011 Hackathon was powered by Gnip data! We love it when our customers find new and creative ways to use the data we provide.

Edward Kim (@edwkim) and Eric Lubow (@elubow) from SimpleReach (@SimpleReach), which provides next generation social advertising for brands, put a team together to develop LinkCurrent, an app powered by Gnip data and designed to measure the current and future social value of a specific URL. When fully developed, the LinkCurrent app will provide the user with a realtime dashboard illustrating various measures of a URL’s worth — featuring an overall social score, statistics on the Klout Scores of people who have Tweeted the URL, how many times the URL has been Liked on Facebook and posted on Twitter, and geo-location information to provide insight into the content’s reach. Call it influence-scoring for web content.

The hackathon team also included Russ Bradberry (@devdazed) and Carlos Zendejas (@CLZen), also of SimpleReach, Jeff Boulet (@properslang) of EastMedia/Boxcar (@eastmedia/@boxcar), Ryan Witt (@onecreativenerd) of Opani (@TheOpanis), and Michael Nutt (@michaeln3) of Movable Ink (@movableink)– Congratulations to everyone who participated! You created an amazing app in less than 20 hours and developed a creative new use for Gnip data. I highly encourage all of you to check it out: www.linkcurrent.co

Have fun and creative way you have used data delivered by Gnip? We would love to hear about it and you could be featured in our next blog. Drop us an email or give us a call at 888.777.7405.

Our customers rock. When they develop code to start using Gnip, they often share their libraries with us so that they might be useful to future Gnip customers as well. Although Gnip doesn’t currently officially support any client libraries for access to our social media API, we do like to highlight and bring attention to some of our customers who choose to share their work. 

In particular, here are a few Gnip client libraries that happy customers have developed and shared with us. We’ll be posting them in our Power Track documentation and you can also find them linked here:

Java
by Zauber
https://github.com/zaubersoftware/gnip4j

PHP
by Socialping
https://github.com/socialping/Gnip-Power-Track-PHP-Classes

Python
by General Sentiment
https://github.com/vkris/gnip-python/blob/master/streamingClient.py

If you’ve developed a library for access to Gnip data and you’d like to share it with us at Gnip and other Gnip customers, then drop us a note at info@gnip.com. We’d love to hear from you.

Social Media in Natural Disasters

September 14th, 2010

Gnip is located in Boulder, CO, and we’re unfortunately experiencing a spate of serious wildfires as we wind Summer down. Social media has been a crucial source of information for the community here over the past week as we have collectively Tweeted, Flickred, YouTubed and Facebooked our experiences. Mashups depicting the fires and associated social media quickly started emerging after the fires started. VisionLink (a Gnip customer) produced the most useful aggregated map of official boundary & placemark data, coupled with social media delivered by Gnip (click the “Feeds” section along the left-side to toggle social media); screenshot below.

Visionlink Gnip Social Media Map

With Gnip, they started displaying geo-located Tweets, then added Flickr photos with the flip of a switch. No new messy integrations that required learning a new API with all of it’s rate limiting, formatting, and delivery protocol nuances. Simple selection of data sources they deemed relevant to informing a community reacting, real-time, to a disaster.

It was great to see a firm focus on their core value proposition (official disaster relief data), and quickly integrate relevant social media without all the fuss.

Our thoughts are with everyone who was impacted by the fires.

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We have some new “Powered by Gnip” badges available for our customers, partners and anyone looking to add something pretty to their website.

The badges are available on our website here: http://www.gnip.com/partners

We have three styles to chose from depending on your color preference, and here is the orange/white style.

powered-by-gnip-whiteonorange

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We just added another company to our list of company solution spotlights and the company is PeopleBrowsr.

peoplebrowsrlogo

PeopleBrowsr is using Gnip for integrating social media data so they can focus on providing the most complete and dynamic tools to their users.  Read more about PeopleBrowsr and other companies use the Gnip platform to put real-time data into their business solutions in the Solution Spotlight page of our Gnip.com.

 

We are thrilled to be working with Collective Intellect, which is a very interesting real time marketing intelligence company and also another local Boulder based company.   Gnip is providing Collective Intellect normalized data access to social media services so they can concentrate on delivering faster, richer, and more actionable market research data to our customers and partners.

Read about how Collective Intellect and other companies are using the Gnip platform in the Solution Spotlight page of our website.

 

collective_intellect_logo

Microsoft .NETNow that the new Gnip convenience libraries have been published for a few weeks on GitHub, I’m going to tell you a bit about the libraries that I’m currently responsible for, the .NET libraries.  So, let’s dive in, shall we… The latest versions of the .NET libraries are heavily based on the previous version of the Java libraries, with a bit of .NET style thrown in. What that means is that I used Microsoft’s Java Language Conversion Assistant as a starting point, mixed in some shell scripting like Bash, Sed and Perl to fix the comments, and some of the messy parts that did not translate very well. I then made it more C# like by removing Java Annotations, adding .NET attributes, taking advantage of .NET native XML Serializer, utilizing System.Net.HttpWebRequest for communications, etc. It actually went fairly quick.  The next task was to start the Unit testing deep dive.

I have to say, I really didn’t know anything about the Gnip model, how it worked, or what it really was, at first. It just looked like an interesting project and some good folks. Unit testing, however, is one place where you learn about the details of how each little piece of a system really works. And since hardly any of my tests passed out of the gate (and I was not really even convinced that I even had enough tests in place,) I decided it was best to go at it till I was convinced. The library components are easy enough. The code is really separated into two parts. The first component is the Data Model, or Resources, which directly map to the Gnip XML model and live in the Gnip.Client.Resource namespace. The second component is the Data Access Layer or GnipConnection. The GnipConnection, when configured, is responsible for passing data to, and receiving data from, the Gnip servers.  So there are really only two main pieces to this code. Pretty simple: Resources and GnipConnection. The other code is just convenience and utility code to help make things a little more orderly and to reduce the amount of code.

So yeah, the testing… I used NUnit so folks could utilize the tests with the free version of VisualStudio, or even the command line if you want. I included a Gnip.build Nant file so that you can compile, run the tests, and create a zipped distribution of the code. I’ve also included an nunit project file in the Gnip.ClientTest root (gnip.nunit) that you can open with the NUnit UI to get things going. To help configure the tests, there is an App.config file in the root of the test project that is used to set all the configuration parameters.

The tests, like the code, are divided onto the Resource objects tests and the GnipConnection tests (and a few utility tests). The premise of the Resource object tests is to first ensure that the Resource objects are cool. These are simple data objects with very little logic built in (which is not to say that testing them thoroughly is not the utmost important.) There is a unit test for each one of the data objects and they proceed by ensuring that the properties work properly, the DeepEquals methods work properly, and that the marshalling to and from XML works properly. The DeepEquals methods are used extensively by the tests, so it is essential that we can trust them. As such, they are fairly comprehensive. The marshalling and un-marshalling tests are less so. They do a decent job; they just do not exercise every permutation of the XML elements and attributes. I do feel that they are sufficient enough to convince me that things are okay.

The GnipConnection is responsible for creating, retrieving, updating and deleting Publishers and Filters, and retrieving and publishing Activities and Notifications. There is also a mechanism built into the GnipConnection to get the Time from the Gnip server and to use that Time value to calculate the time offset between the calling client machine and the Gnip server. Since the Gnip server publishes activities and notifications in 1 minute wide addressable ‘buckets’, it is nice to know what the time is on the Gnip server with some degree of accuracy. No attempt is made to adjust for network latency, but we get pretty close to predicting the real Gnip time. That’s it. That little bit is realized in 25 or so methods on the GnipConnection class. Some of those methods are just different signatures of methods that do the same thing only with a more convenient set of parameters. The GnipConnection tests try to exercise every API call with several permutations of data. They are not completely comprehensive. There are a lot of permutations. But, I believe they hit every major corner case.

In testing all this, one thing I wanted to do was to run my tests and have the de-serialization of the XML validate against the XML Schema file I got from the good folks at Gnip. If I could de-serialize and then serialize a sufficiently diverse set of XML streams, while validating that those streams adhere to the XML Schema, then that was another bit of ammo for trusting that this thing works in situations beyond the test harness. In the Gnip.Client.Uti namespace there is a helper class icalled XmlHelper that contains a singleton of itself. There is a property called ValidateXml that can be reached like this XmlHelper.Instance.ValidateXml. Setting that to true will cause the XML to be validated anytime it is de-serialized, either in the tests or from the server. It is set to true in the tests. But, it doesn’t work with the stock Xsd distributed by Gnip.That Xsd does not include an element definition for each element at the top level which is required when validating against a schema. I had to create one that did. It is semantically identical to the Gnip version; it just pulls things out to the top level. You can find the custom version in the Gnip.Client/Xsd folder. By default it is compiled into the Gnip.Client.dll.

One of the last things I did, which had nothing really to do with testing, is to create the IGnipConnection interface. Use it if you want. If you use some kind of Inversion of Control container like Unity, or like to code to interfaces, it should come in handy.
That’s all for now. Enjoy!

Rick is a Software Engineer and Technical Director at Mondo Robot in Boulder, Colorado. He has been designing and writing software professionally since 1989, and working with .NET for the last 4 years. He is a regular fixture at the Boulder .NET user’s group meetings and the is a member of Boulder Digital Arts.

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