Gnip is always looking for ways to improve its filtering capabilities and customer feedback plays a huge role in these efforts.  We are excited to announce enhancements to our PowerTrack product that allow for more precise filtering of the Twitter Firehose, a feature enhancement request that came directly from you, our customers.

Gnip PowerTrack rules now support OR and Grouping using ().  We have also loosened limitations on the number of characters and the number of clauses per rule. Specifically, a single rule can now include up to 10 positive clauses and up to 50 negative clauses (previously 10 total clauses).  Additionally, the character limit per rule has grown from 255 characters to 1024.

With these changes, we are now able to offer our customers a much more robust and precise filtering language to ensure you receive the Tweets that matter most to you and your business.  However, these improvements bring their own set of specific constraints that are important to be aware of.  Examples and details on these limitations are as follows:

OR and Grouping Examples

• apple OR microsoft
• apple (iphone OR ipad)
• apple computer –(fruit OR green)
• (apple OR mac) (computer OR monitor) new –fruit
• (apple OR android) (ipad OR tablet) –(fruit green microsoft)

Character Limitations

• A single rule may contain up to 1024 characters including operators and spaces.

Limitations

• A single rule must contain at least 1 positive clause
• A single rule supports a max of 10 positive clauses throughout the rule
• A single rule supports max of 50 negative clauses throughout the rule
• Negated ORs are not allowed. The following are examples of invalid rules:
• -iphone OR ipad
• ipad OR -(iphone OR ipod)

Precedence

• An implied “AND” takes precedence in rule evaluation over an OR

For example a rule of:

• android OR iphone ipad  would be evaluated as apple OR (iphone ipad)
• ipad iphone OR android would be evaluated as (iphone ipad) OR android

You can find full details of the Gnip Power Track filtering changes in our online documentation.

Know of another way we can improve our filtering to meet your needs?  Let us know in the comments below.

Last night proved a great example of this as word of Huntsman’s withdrawal from the GOP presidential race crept out. Interestingly, the news was broken by Peter Hamby, a CNN Political Reporter–on Twitter. While CNN followed up on this news a few minutes later, it seems the reporter (or the network) realized the inherent ‘newswire’ value of breaking this news as fast as possible…and used Twitter as part of their strategy to do so!

This Tweet was followed with what we’ve begun to see as the normal ‘Twitter’ spike for breaking news – the chart below, built by our Data Scientist Scott, shows how quickly Huntsman withdrawl was retweeted and passed along. When looked at in comparison to an aggregate news feed (in this case, NewsGator’s Datawire Firehose, which is a content aggregator derived from crowdsourced rss feeds and contains many articles from traditional media providers), some interesting comparisons are brought to light.

Comparing tweets of “huntsman” and news articles breaking Jon Huntsman’s withdrawal from GOP primary race. The blue curves show the “Social Activity Pulse” that characterizes the growth and decay of media activity around this topic. By fitting the rate of articles or tweets to a function we can compare standard measure such as time-to-peak, store half-life etc. (More on this in a future post.) The peak in Twitter is reached about the same time as the first story arrives from NewsGator, over 10 minutes after the story broke on Twitter.

Both streams show a similar curve in story adoption, peak and tail. What’s different is the timeframe of the content. Twitter’s data spikes about 10 minutes earlier than NewsGator’s. NewsGator’s content is more in-depth, as it contains news stories and blog posts, but as we’ve seen in other cases, Twitter is the place where news breaks these days.

As we enter 2012, Facebook users are more optimistic than Twitter users.

You’re probably wondering how we can say that. Well, we looked at all of the public posts on Facebook and Tweets on Twitter that contained “Happy New Year.” For all of those posts and Tweets, we compared the use of positive words such as “better” and “good” to the use of negative words such as “worse” and “bad.” We found that Tweets with positive words appeared 8 times more frequently than Tweets with negative words. You might be thinking a ratio of 8 to 1 is pretty optimistic…

It may be, but posts on Facebook had a ratio of 40 to 1–such a huge difference lead us to speculate that Facebook is a more optimistic place than Twitter.

Interesting stuff. Could be a variety of reasons for the difference, from the mix of users on each service to the fact that Facebook is used to communicate with friends, while Twitter is user to broadcast to followers. We’ll leave the speculation up to you.

“66% of respondents who bought something on Black Friday did so as a direct result of social media interactions with friends and family.”

While that stat speaks to the impact social media has upon us as individuals, think more broadly about how powerful it is to analyze that data in aggregate, in real-time. Companies are leveraging data from WordPress blogs, Twitter mentions, Facebook likes and multiple other sources to inform critical realtime decisions for inventory management and operational planning, sales and marketing planning, revenue forecasting, and many others.

Example Scenario for Using Social Data: It’s holiday time, 2011. Your company begins to aggregate ‘mentions’ of a new product from Twitter, Facebook, WordPress blogs in realtime. You take that data and analyze it for # of mentions about the new product, geography of posts (where available), demographic information within user profiles (what keywords are most consistent within Twitter user profiles that mentioned your product?), etc.

You spread that data among multiple divisions, providing additional forecast, regional buying pattern, and customer habit data. Your teams use that to:

1. Manage supply chain: Redirect inventory to areas with highest potential sales and (depending on how far out you are) use as a data point in the S&OP system for manufacturing forecasts to keep ahead of the holiday demand.
2. Target marketing spend: Use regional buying patterns and customer habit data to inform what demographic you are, and aren’t, hitting. Do you need to reposition your marketing plan?
3. Incorporate product feedback: Are there consistent reasons why people are buying your product – or why they aren’t? Information on quality, packaging, price, etc will be incredibly valuable for future products.
4. Calibrate investor expectations: Inform your IR team of potential positive/negative performance feedback to give them running room ahead of any announcements.

Those are just some of the more common use cases we’re seeing. But new opportunities are popping up on a daily basis. We spotted this gem in a recent WSJ article about finding a parking space during crazy shopping times:

Bud Kleppe, a real-estate agent in St. Paul, Minn., watches Mall of America’s Twitter feed for parking updates. (The mall sends them out under the hash tag #moaparking.)

Imagine collecting data from update systems like this and using it measure parking turnover across prime shopping days. Now, overlay the turnover of spots in specific sections against a map of stores and you have some interesting potential for data on economic performance and forecasting. When incorporated with other traditional retail data and compared on a store-to-store basis, you’ve built a unique and realtime analysis tool.

You’re only limited by your imagination in how you can apply social media data to you business. The more software developers, corporations, and people use social media, and the more things they use it for (like parking updates!), the greater the possible use cases for analysis of that data and the more valuable it becomes.

Welcome to the Battle of the Pies!

For those of you that have been in a pie eating contest or had a pie in the face, you know this one will be a fight all the way down to the very last crumb. In one corner (well actually it is the Gnip Octagon so can you really have corners, oh well) we have The Traditionalist, pumpkin pie and in the opposite corner, The New Comer, pecan pie. Without further ado, Ladies and Gentleman, Let’s Get Ready to Rumble, wait wrong sport. Let’s Fight!

Six Social Media Sources, Two Words, One Winner . . . And the Winner Is . . .

Source	Pumpkin Pie	Pecan Pie	Winning Ratio   Pumpkin Pie to Pecan Pie
Twitter	X		4:1
Facebook	X		5:1
Google+	X		6:1
Newsgator	X		3:1
WordPress	X		5:1
WordPress Comments	X		2:1
Overall	+6 Winner!	+0

We looked at one week’s worth of data across six of the top social media sources and determined that pumpkin pie “takes the cake” (so to speak) across every source.

In this case, it is interesting to point out that in sources like Twitter, Facebook, Google+ and WordPress we see higher winning ratios, while sources that tend to have higher latency such as Newsgator and WordPress Comments were a little more even. Is this because, on further consideration, pecan pie sounds pretty good? Or is it that everyone will have to have two pies and, with pecan as the traditional second, it is highly discussed?

Top Pie Recipes

Even though pumpkin pie was our clear winner, we thought it would be fun to share a few of the most popular holiday pie recipes by social media source:

1. Twitter – Cook du Jour Gluten-Free Pumpkin Pie and Pecan Pie Video Recipe from joyofcooking.com
2. Facebook – Ben Starr’s Pumpkin Bourbon Pecan Pie Recipe
3. Newsgator – BlogHer’s Pumpkin Pecan Roulade with Orange Mascarpone Cream Pie Recipe
4. WordPress and WordPress Comments – Chocolate Bourbon Pecan Pie from allrecipes.com

Non-Traditional Thanksgiving Pies

Another interesting fact that came out of this Cagefight was the counts of non-traditional Thanksgiving pies that were mentioned across the social media sources we surveyed. Though we rarely find these useful for communicating numerical values effectively, you can’t not have a pie chart in this post.

Happy Thanksgiving!

Over the past year, we’ve seen extraordinary growth in the number of paid social media activities we deliver. At the start of 2011, Gnip was delivering 300 million activities per month.  By May, that number was up to 3 billion activities per month.  And in October, we delivered 30 billion activities.  In essence, we’ve been growing by a factor of 10 every 5 months.  At this rate, we’ll be delivering 300 billion activities per month by March of next year

Cool numbers, but what’s driving this growth?

We’re seeing three key areas that are driving this number. First, we’re signing on new customers at an increasing rate, as more and more companies are seeing the possibilities in social media data. Second, we’re seeing increased interest in our Twitter firehose products. From hedge funds using social data to drive trading strategies to business intelligence companies layering social data onto their existing structured data sources, interest in volume products from Twitter is consistently increasing.  And finally, we’re seeing a marked increase in the number of customers using multiple sources to enrich their product capabilities.  From boards and forums to YouTube and Facebook, our customers are seeing the potential in the many other social media sources we offer.

So, 300 billion per month by March? It’s a big number, but the way things are going, I’ll take the over.

Gnip is excited to announce the addition of Google+ to its repertoire of social media data sources. Built on top of the Google+ Search API, Gnip’s stream allows its customers to consume realtime social media data from Google’s fast-growing social networking service. Using Gnip’s stream, customers can poll Google+ for public posts and comments matching the terms and phrases relevant to their business and client needs.

Google+ is an emerging player in the social networking space that is a great pairing with the Twitter, Facebook, and other microblog content currently offered by Gnip. If you are looking for volume, Google+ quickly became the third largest social networking platform within a week of its public launch and some are projecting it to emerge as the world’s second largest social network within the next twelve months. Looking to consume content from social network influencers? Google+ is where they are! (even former Facebook President Sean Parker says so).

By working with Gnip along with a stream of Google+ data (and the availability of an abundance of other social data sources), you’ll have access to a normalized data format, unwound URLs, and data deduplication. Existing Gnip customers can seamlessly add Google+ to their Gnip Data Collectors (all you need is a Google API Key). New to Gnip? Let us help you design the right solution for your social data needs, contact sales@gnip.com.

In addition to crowning the victor, we’ll also call out some of the fun, interesting, strange, and bizarre trends that we glean from the data. Leave us a comment with any contenders you’d like to see in the future.

Now without further delay, let’s dive into our first Gnip Cagefight… Put your hands together for Wine vs. Beer!

And the Winner is . . .

We looked at one week of Tweets that contained the words “beer” or “wine,” and beer was the more commonly used term, appearing in 53.1% of those tweets vs. 48.1% for wine. Now you might be saying, “Hey, that’s more than 100%!” You are correct! That’s because beer and wine appear together about 13,801 times–along with an uncomfortable hangover, we presume. (Is this an opportunity to sell aspirin?)

With beer as our victor, we wanted to answer the age old question . . .

What time is Beer Thirty?

To answer this question, we analyzed the volume of Tweets containing the term “beer” throughout each day and averaged that across the week’s worth of data we collected. Each Tweet’s time was moved into the time zone of the Tweeter and normalized against the daily cycle of Tweet volume. Based on the graph below, true beer thirty is 5pm local time. This gives great meaning to the saying “It’s 5 o’clock somewhere.”

Beer Drinkers have a Wider Vocabulary than Wine Drinkers

Another fascinating tidbit that came out of the data was that beer drinkers have a wider vocabulary than wine drinkers. Normalizing for the number of words used, we find that beer drinkers use 14% more distinct words than wine drinkers. Wine drinkers tend to use the same idioms, for example, “glass of wine” or “red wine,” more than beer drinkers use their most common phrases. Does this mean that beer drinkers are 14% smarter than wine drinkers? Or that they use very creative spelling? We won’t wade any further into that question, but you can be the judge.

That’s all for our inaugural Gnip Cagefight. Hope you enjoyed it and be sure to let us know what what words you’d like to see in the octagon in the future.

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.

Looking at the content of those Tweets, you see expressions of sadness and loss, thanks for everything he did, and a celebration of his genius and talent.  All sentiments we felt here at Gnip.

Thank you for everything Steve.  The world is a poorer place without you.  Rest in peace.