Tag: backtype

Risk Assessment: Corporate Acquisitions Can Kill APIs

So it seems that my to-do list just got shorter as Twitter acquire BackType and as a result “will discontinue the BackType product and API services”.

Bah…:-(

On my roadmap (err, such as it is!;-), one thing I was hoping to do was start exploring in more detail the struture of communities around a shared link, with a view to exploring in more detail some of the actual dynamics of link sharing across Twitter networks. My early forays in to this have tended to use BackType, as for example in Visualising Ad Hoc Tweeted Link Communities, via BackType.

The simple recipe I’d started out with was based around the following steps:

– given the URL, look up who’s tweeted it via the BackType API;
– for each tweeter of the link, grab the list of people they follow (i.e. their friends);
– plot the “inner” network showing which of the people who tweeted the link the follow each other.

This gave an easy way in to identifying a set of folk who had expressed an interest in a link by virtue of sharing it, this set then acting as the starting point for a community analysis.

Another approach I started to explore (but never blogged?!) was looking at networks of folk who had shared one of the links recently shortened by a particular bit.ly user. So for example, this graph (captured some time ago) used the BackType API to find who had tweeted one of more of 15 or so links that @charlesarthur had shortened using bit.ly, and then plotted friend connections between them:

follower connections between folk tweeting one or more of 15 links also recently shortened on bitly by charlesarthur

Unfortunately, now that the BackType API has gone (when I try to call it I get a “Limit exceeded” error message), the key ingredient from those two original recipes is no longer available…:-(

Visualising Ad Hoc Tweeted Link Communities, via BackType

So you’ve tweeted a link as part of your social media/event amplification strategy, and it’s job done, right? Or is there maybe some way you can learn something about who else found that interesting?

Notwitshtanding the appearance of yet another patent of the bleedin’ obvious, here’s one way I’ve been experimenting with for tracking informal, ad hoc communities around a link. (In part this harkens back to some of my previous “social life of a URL” doodles such as delicious URL History – Hyperbolic Tree Visualisation, More Hyperbolic Tree Visualisations – delicious URL History: Users by Tag.)

In part inspired by a comment by Chris Jobling on one of my flickr Twitter network images, here’s a recipe for identifying a core community that may be interested in a retweeted link:

– given the URL, look up who’s tweeted it via the BackType API;
– for each tweeter of the link, grab the list of people they follow (i.e. their friends);
– plot the “inner” network showing which of the people who tweeted the link the follow each other.

To explore the possible reach of the tweeted link, grab the followers of each person who tweeted the link and plot that network. This is likely to be quite a large network, so you may want to prune it a little, for example by filtering out everyone with node degree less than two.

So for example, earlier today I spotted a tweet about an OU philosophy game (To Lie or Not to Lie?), which I also retweeted. Here’s what the “inner” retweet graph looks like at the moment:

The node size is related to degree, the colour to total follower count. The graph can be used to identify a core network of folk who may be willing to promote OU activities (maybe…?!;-)

The next image shows the retweeters and their followers, filtered to show followers with degree 2 or more (the “double hit” audience). [Actually, I should filter on ((out-degree > 0) and (degree > 1 and in-degree > 1)).]

The nodes are partitioned into clusters using the Gephi modularity statistic and coloured accordingly. Node size is related to total follower count. Layout is done using an expanded Yifan Hu layout.

In a follow on post, I’ll show how we can generate network maps for people on delicious who either bookmarked a particular URL, or follows someone who did…