How to visualise Twitter data in 5 minutes

How to visualise Twitter data in 5 minutes

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Today I’ll show how you can visualise Twitter data (for example for a local SEO conference as I did in this example) to generate nice curated content and display trends. Hopefully you’ll find this as useful as I did.

Here’s a taster of how the graphs you create today may look:

Slightly more advanced graphs

Slightly more advanced graphs

Minutes 1-2

Go to and set yourself up with a free account. Then do the same on

ScraperWiki we’ll use for scraping twitter data and data hero for displaying it nicely.

Minutes 3-4

Go to your new ScraperWiki account:

  1. Click on ‘Create a new dataset’
  2. ‘Search for new followers’
  3. Refer to this list when creating your query for the next screen, mine was: #brightonseo since:2013-09-12 until:2013-09-14
  4. Then the app will ask you to authorise it via your Twitter account
  5. Then press on ‘View as a table’
  6. Then ‘Download as a spreadsheet’ and download ‘all_tables.xls’
  7. Review the data to ensure it looks natural – ie. a big event attracts hundreds of tweets or thousands, so if you only have 50 there’s something wrong. You may have to re-run the scraper using different settings. Also remove irrelevant days (by deleting rows) if you only want data for a given day.

Minutes 4-5

Go to your DataHero account, login and click on ‘Upload’ next to ‘your data’.

  1. Choose the .csv export from ScraperWiki
  2. You will be suggested charts based on it, I prefer to choose ‘Create New Chart’
  3. Then drag and drop data you’d like to visualise to the space on the right.
  4. This is the kind of a screen you should be seeing after dropping in two or even one bit of data, it really depends what you’d like to visualise, for a start I suggest just using ‘created at’ column to see at what times there were the most tweets:

    Basic Chart from DataHero

    Basic Chart from DataHero

  5. Once you’re done with a particular graph press on Export and voila you have your graphical representation of Twitter data.


That’s it – now you have your first basic graph. I encourage you to experiment with different bits of data and graphs and colours.

The data for me wasn’t 100% accurate, but it was enough to show the trends etc. Here’s the end effect of a few graphs that took me about 30 minutes or so

For full versions of the graphs I generated see the original at our Jellyfish blog.

I really hope you enjoyed this post and will give it a try. Please let me know if you figure out better ways to use the data or find any mistakes in what I’ve done – I only learned this today.

This is heavily based on this post, which I read thanks to Gisele today (Thanks again Gisele!)


Categories: SEO How to


  1. This is pretty cool dude, this might come in handy for some of the other major SEO conferences around the UK as well?

    I guess it might also be useful for brands looking to monitor a Hash Tag Campaign?

    Good work

    Kevin Wiles
    1. Oh ye good point, I didn’t think about Hash Tag Campaign monitoring – during the day and post event to see where the spikes happen! Thanks for that suggestion.

      Krystian Szastok
    1. Thanks for the comment mate, yeah I’m thinking of covering an event this way.

      A full week may be too much data for this tool, but I’ll give it a shot! May lead to another post as I may need a bigger scraper/data tool.

      Krystian Szastok

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