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Published June 30th 2011

Wimblewatch 2011: A Brandwatch DataViz

We thought we’d use Brandwatch to keep an eye on a few key topics on Twitter during the course of Wimbledon, to see what and who is being most talked about and when.

To keep in the spirit of the event and to bring the data to life, we came up with this visualisation:

Wimblewatch 2011

(It was built with Processing, because of course we like to be cutting edge…this means you’ll need Java installed to view it.)

What is it showing?

Most simply, the visualisation is showing two things – when the conversation happened about a certain topic (horizontal axis), and what score those conversations were given (vertical axis).

The score is based on a combination of the number of followers the author of the tweet had and whether the tweet was positive, negative or neutral in content (see below for more explanation).

Annotated screenshot:

Whare are the dots?

Each dot represents one or more instances of a tweet about a topic on a certain day which we gave a certain score. The bigger the dot, the more tweets we found of that topic on that day with the same score.

So, if you select Federer, for example, you see a huge dot on 29th June when he lost against Tsonga:

What does the positioning of the dots mean?

The horizontal positioning is the day those tweets were made, we show you data from the last 8 days.

The vertical positioning is as follows:

  • If the dot is in the top half (0 to +5) then it means those tweets would have a positive effect for that player/topic because they were of positive content and the people who tweeted them had a high number of followers. The higher up the stronger that positive effect.
  • (If you were a brand tracking tweets about you, these would be the ones you want most of!)
  • If the dot is around the middle (0) then it can mean one of two things, either: the tweets were neutral in content and so have no positive/negative effect regardless of the number of followers the author had, or: the tweets were positive/neutral in content but the author has a low number of followers and so the tweet has minimal effect.
  • (If you were a brand tracking tweets about you, these would be the ones you’re not too bothered about.)
  • If the dot is in the bottom half (0 to -5) then it means those tweets would have a negative effect for that player/topic because they were of negative content and the people who tweeted them had a high number of followers. The higher up the stronger that positive effect. 
  • (If you were a brand tracking tweets about you, these would be the ones you’re most worried about!)

We hope you enjoy it!

This is the first data visualisation we’ve made of this kind – if you have any ideas for what you’d like to see in the future or how we could improve it, we’re always happy to hear from you at [email protected].

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