Australian data team blog How we used machine learning to cover the Australian election


by Nick Evershed and Josh Nicholas

15 July 2022


All up, we processed 34,061 Facebook posts, 2,452 media releases, and published eight stories (eg here, here and here) in addition to an interactive feature. We also used the same Facebook data to analyse photos posted during the campaign to break down the most common types of photo ops for each party, and how things have changed since the 2016 election.


We were able to discover more than 1,600 election promises, amounting to tens of billions of dollars in potential spending. Our textual analysis later found almost 200 (112 in marginal seats) of the Coalition’s promises were explicitly conditional on their winning the election. This means much of the targeted-largesse may never have been widely known without our project.


Teasing out a few hundred election promises from millions and millions of words is like finding a needle in a haystack, and would have been otherwise impossible for our small team in such a short time frame without making use of machine learning.



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