During the last Australian election we ran an ambitious project that tracked campaign spending and political announcements by monitoring the Facebook pages of every major party politician and candidate. The project, dubbed the "pork-o-meter" (after the term pork-barreling), was hugely successful in being able to identify distinct patterns of spending based on vote margin, or incumbent party, with marginal electorates receiving billions of dollars more in campaign promises than other electorates. 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.

The novelty cheque has long been a mainstay of the political "photo op" but a Guardian Australia analysis of photos posted during the 2022 and 2019 election campaigns suggests giant cheques are on the way out, while hi-vis workwear and photos of dogs are on the rise. During our work building the automated systems behind the pork-o-meter, which tracks election campaign pork barrelling as it occurs, the Guardian's data team found ourselves asking an important question. Could we teach a robot to spot photos of novelty cheques? We were already using machine learning to flag text from politicians' Facebook posts as likely grant announcements and election promises, but having another model in place to find big cheques and certificates in photos might pick up things we'd missed in the text.