Which Australian Political Leader Has The Highest Number Of Fake Twitter Followers?

Like 'D-Grade' influencers, are politicians also buying their own followers?

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In the endless popularity contest that is Australian politics, having a strong social media following can be the difference between re-election or shadow ministry.

But in this dark age of deep fakes and bot accounts, how can we trust that the political leaders in this country are using social media in good faith, and not enlargening their digital presence through shady tactics such as buying likes on social media?

We decided to crunch the numbers to reveal once and for all which Australian political leader had the highest number of phoney Twitter followers. Our investigation canvased the leaders of the biggest political parties running in the 2022 Federal Election, Labor, The Liberal Party, The Nationals, Pauline Hanson’s One Nation, The Greens and The United Australian Party.

The method for our investigation relied heavily on the application Twitter Audit, which extrapolates the number of “fake” followers attached to an account by using an advanced series of algorithms to scan a sample size of the target account’s followers.

Before you check out the ranking, Junkee emphatically stresses that this ranking is merely an estimate, and by no means factual evidence of any wrongdoing or misconduct of any kind.

Without further ado, here’s what we found:

  1. Scott Morrison (The Liberal Party) 24.7 percent Fake Followers
  2. Antony Albanese (Labor Party) 21.5 percent Fake Followers
  3. Adam Bandt (The Greens) 15.9 percent Fake Followers
  4. Barnaby Joyce (Nationals) 15.8 percent Fake Followers
  5. Pauline Hanson (One Nation) 10.4 percent Fake Followers
  6. Craig Kelly (Australia United Party) six percent Fake Followers

Interestingly, the ranking of fake followers perfectly matches the descending order of how many total followers each leader has, with Scott Morrison at the top with 652,820 total Twitter followers and Craig Kelly at the bottom with 71,344 followers.

There are a few explanations for this. Higher follower counts attract more spam accounts, this is especially true of people who hold important roles in the public eye. The margin of error used in the program also scales with the number of total followers the target account has, presumably finding “more” fakes in accounts with higher followers.

After this examination, we began to randomly scan other Australian political candidates for fake followers. The worst offender TwitterAudit detected was Tim Coombes, a United Australia candidate for the Queensland seat of Oxley.

More than half of Coombes’s total 4,330 followers were detected as fakes, with Twitter Audit detecting 2568 followers as potential bot accounts.  Mysteriously, Coombes has accrued more followers than some of his fellow Queensland candidates, especially since his account was only created in March this year.

Taking a superficial look at Coombes followers, many use non-sensical usernames and profile pictures while also having low follower numbers themselves.

Junkee has reached out to Coombes for an explanation but is yet to receive a reply.

It seems like politicians have mostly learned the lesson that teenage influencers discover early in their careers: it doesn’t pay to buy fake followers. Not only are fakes usually pretty easily detected, but they also eschew your target audience in ways unfitting for the account holder.

However, if the data is to be believed, what is disturbing about the findings is just how many fake accounts are out there and how they flock to popular political candidates.

Like the Cambridge Analytica scandal which used innocuous personality quizzes to harvest Facebook data and ultimately influenced the UK’s Brexit vote, the full power of these spam bot accounts is yet to be discovered.

Let’s hope that the 10 to 20 percent of our major political leader’s followers which could potentially be bot accounts lay dominant for now, or at least delay their plans for a spam uprising until after the 2022 Federal Election.