“Twitter track on steroids” announced by TweetMeme founder

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If you follow Steve Gillmor you know he loved a feature that used to be in Twitter, that he called Track. It let you ask Twitter to shove you real-time info. It was turned off as Twitter tried to deal with its scale problem.

But now Twitter is bringing back real-time streams. I’m using a pre-release version of TweetDeck that shows tweets coming into its columns in real time. It moves fast.

And that’s the problem. We all want to follow more and more people but that means we are less and less likely to keep up with the people and information we really want to keep up with.

So, how do we keep up?

Well, Nick Halstead has an answer he calls DataSift. He called it “track on steroids” when he first told me about it. I immediately got excited and turned on my camera.

It is in final stages of development, he told me, and will be available to developers in the next month. Nick says that Twitter worked with him on its development and it sounds exciting, there’s more on the Datasift blog. Listen to the video, but here’s how Nick pitched it to me in Skype before I turned on the camera:

datasift – takes the twitter firehose – and gives you twitter lists for content
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its twitter track on steriods
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so alerts/analytics/and API access to take real-time streams of the curated content.
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built around a whole new programming language to tell it what content you want to extract
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so for example,  – twitter.user.follow_count > 5000
would give you a stream of all the updates from twitter users with more than 5000 followers
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– twitter.geo GEO_RADIUS “51.500789,-0.142264:10.0” AND twitter.text CONTAINS “coffee”  AND twitter.sentiment > 75 looks for people in a 10km range of SF – that mention cofee – and are being very positive about it
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looks for people in a 10km range of SF – that mention cofee – and are being very positive about it any data that is contained in a tweet (of which there are like 30 different fields) can be filtered against
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any data that is contained in a tweet (of which there are like 30 different fields) can be filtered against, including annotations

UPDATE: Nick has more on his personal blog about the new Twitter button feature, and yet more, along with a video, on the DataSift blog.

Published by Robert Scoble

I help entrepreneurs build their technology business' story, help with getting ready for investors, with other launch plans, and many other strategic things that can help your new startup. Call to talk: +1-425-205-1921 (text first).

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15 thoughts on ““Twitter track on steroids” announced by TweetMeme founder”

  2. I like this dude a lot. It has me excited again. It's like of course he's got to make money and maybe he'll make a ton, and there's nothing wrong with that, but he seems to be more interested in doing cool stuff, creating cool stuff as his top priority. And features like this are where the real value of social media is, the real coolness of it, and not all this other you know High Schoolish popularity contest kind of thing. So happy there are dudes like this.

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  3. Thanks for this 🙂 DataSift is really interesting. If they can execute it well I can see it being extremely powerful.

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  5. Truely what the twittersphere needs, alot more of a granular approach would bubble up so much more quality content, mixin retweets – geolocation – .follow_count etc and we have a powerful language for twitter. Seems quite like the yahoo query language, this should be very cool !!!. Nice work on the scoop Scoble.

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  9. WOW! that’s a pretty useful tool for isolating and extracting data from twitter and it’s users. i think the possibilities and applications could be endless. i’m curious to see just how data sift is used in the first few months after its release.

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  11. Truely what the twittersphere needs, alot more of a granular approach would bubble up so much more quality content, mixin retweets – geolocation – .follow_count etc and we have a powerful language for twitter. Seems quite like the yahoo query language, this should be very cool !!!. Nice work on the scoop Scoble.

    Like

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