Following up this analysis, even though @CraigKellyMP only sent 114 (0.001%) of the anti-lockdown campaign tweets, he received a whopping:

- 28% of all likes
- 25% of all replies
- 26% of all quoted retweets.

This diagram shows the top accounts by engagement metrics 🧵
But when it comes to retweets, Kelly's metrics are overshadowed by the amplification efforts of a coordinated network of fringe protestor accounts, as this chart shows:
We see that Kelly doesn't appear in the coordinated behaviour network (see original tweet above). Rather, his tweets frame the agenda and injects a massive amount of fuel into the Twitter protest networks *but* he doesn't coordinate his activity with them.
Looking at the creation dates of accounts tweeting these hashtags, yet again we see a very high concentration of newly created accounts. This tree map shows that most accounts are created in the past couple of months. One-fifth of all accounts were created this year.
In itself this is not necessarily suspicious as many people may have joined Twitter during the pandemic. *But* these results do echo the findings of our previous work, where newly created accounts were associated with suspicious platform manipulation:
theconversation.com/istandwithdan-…
In short: we have @CraigKellyMP, a politician funded by the taxpayer, demonstrably turbocharging and framing the agenda for a coordinated network of anti-lockdown and anti-government Twitter accounts, many of which are anonymous and created recently.

*by engagement metrics
I hope this analysis is of benefit to the community. As always, if anyone would like tweet IDs or details of methodology, please DM!

Data collected using 'twarc2': twarc-project.readthedocs.io/en/latest/
Postscript: the pro-Andrews activists, led by @PRGuy17, managed to get their own hashtag into the #1 spot in only 25 minutes

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More from @timothyjgraham

4 Oct
Folks messaged me about a new hashtag anti-Andrews campaign trending, so I collected over 67,000 tweets (past 7 days) containing the relevant hashtags and ran the data through a coordinated behaviour detection system

A few insights, starting with a network map 🧵
The network shows large-scale, loosely coordinated behaviour by a hard core of campaigners who spam the hashtags repeatedly together, all day long.

BUT it's @CraigKellyMP and @OzraeliAvi who jump onto the fringe activity and turn it into a roaring blaze with two viral tweets
A pro-Andrews counter-attack started almost immediately, orchestrated by @PRGuy17, in an effort to hijack the hashtags and drown out the anti-Andrews discourse
Read 13 tweets
1 Aug
New study reveals private groups behind the 'pl*ndemic’ disinformation campaign. These groups not only coordinated a surge in COVID-19 conspiracy theories, but also "coached" citizens into fanatic activism against COVID-19 measures. frontiersin.org/articles/10.33…
The study found that this was driven mostly by small-reach conspiracy theorists all posting at once:

“The most influential Twitter users … appear to be either citizens or activist accounts, rather than bots”
Also interesting:

“And the most common word in more than half of all top users' profile descriptions was “truth.” These profile descriptions often signal a search for a “hidden truth,” as if they are part of a citizen initiative to purge the world of evil actors.”
Read 4 tweets
5 Jan
Just to clarify, the primary interest here is Twitter's design features and dynamics, and how a single account (@PRGuy17) managed to get a hashtag to #1 on the Australia trending list in less than 1 hour

(1/5)
I ran sentiment analysis to provide a quick comparison with our peer-reviewed study of hashtag publics during #Covid19Vic, where we found that pro-Andrews tweeters were overwhelmingly positive and anti-Andrews tweeters were overwhelmingly negative:

journals.sagepub.com/doi/full/10.11…

(2/5) Image
But I agree with criticism of VADER and similar tools, especially limitations for detecting irony and sarcasm. Folks are right to question it

And we acknowledge its limitations in the paper above and highlight its usefulness at scale as part of a broader suite of methods

(3/5)
Read 5 tweets

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