A data visualisation to divide the nation

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SBS's remarkable reality TV series on refugees and asylum seekers called Go Back exploded on Twitter with over 36 000 Tweets last week. We thought it would be cool to analyse the Tweets and try distill the sentiment of a nation (or at least the Twitter savvy of this nation) on this issue that apparently divides the population.

Partnered with Alterian using their business intelligence product SM2 to extract all the information on each Tweet, a database of all word pairs was created (over a million!). The word pairs, like 'asylum seekers' or 'boat people', were generated from each Tweet independently and then tallied up over all Tweets. Common words like 'the' and 'like' were removed, and a stemming algorithm was used to group words such as 'Australia', 'Australian', or 'Australia's' together. All Tweets were treated equal and all Retweets were included so that the content of the most popular and followed people on Twitter would emerge via Retweets.

Once the top word pairs (based on a tally) were finalised an open source software called Gephi, which is a powerful tool for visualising and analysing large networks, was used to present the data. See below for our first attempt; each word is connected to the words that were paired with it, taken from the the top word pairs. The size of the words is related to how many other words are connected to it (not how mant times the word pair appeared in all Tweets).

The whole network is below and shows how the different words are associated. The word 'Raquel' is at the centre (and is the largest) because it was associated with the most words. Many interesting word associations come out of the data. For example, there is a sub-network with words 'live', 'exports', and 'corners' (top left) most probably comparing the SBS Go Back series to the Four Corners program that exposed the live exports trade.



One thing that you might notice is that there are pockets of networks that are associated with a particular Tweet that was Retweeted a lot. For example the Tweet below is associated with the sub-network (bottom middle) that contains words such as 'no', 'vote', 'mad', 'point', and 'court'. You can search Google with any combination of associated words along with the word 'gobacksbs' to find the Tweets that made up the data.


A focus on the main star of the program Raquel shows that the word 'Raquel' was often used in Tweets along with words like 'ignorant', 'racist', and 'complain', but also the words 'hope', and 'change'. This surely reflects the change in viewer sentiment for Raquel as she modifies her views on refugees and Africans, and shows compassion, over the three-part series.



An interesting set of word pairs...



In our next installment we will try, among other things, to see what comes out if Retweets are not used. We will also visualise the number of times each word pair occurred making the line joining words thicker if it occurred a lot. There is a lot of scope for further analysis as Alterian's SM2 provides data on things like the gender of people who Tweeted, where in the world they are from, and when they Tweeted. See below for a screenshot of SM2's interface:



We hope you like it. Let us know what you think in the comments section below. Stay tuned.



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