Buckle up, it's time for a wild ride into #FluTrucksKlan Twitter. 🧵

Just under 100K tweets using pro-convoy hashtags were collected. Since that's far too much text to read and analyze, I built a bi-term topic model to distill the content of the tweets down into 70 topics.

1/
The topic model assumes that:
1. that tokens (words/hashtags/@) that co-occur in tweets are more likely to belong to the same topic;
2. each tweet represents one or more topics;
3. each user's body of tweets is a mixture of topics.

2/
Other notes:
The dataset is mostly pro-convoy (I'd estimate around 95%) tweets, though there are some anti-convoy posters represented because it's super difficult to filter them out completely based on hashtags and keywords.

3/
Since the topics include user tags, any accounts that don't belong to public figures have been removed. A figure being tagged also doesn't necessarily indicate support for the convoy, since critical voices also made it into the data.
4/
See the distribution of topics and the top terms in the convoy tweets. Topic 1 is a junk collection topic, so it isn't included. It might be best to download the chart for a good look in high-res. The longer the line, the more important the topic is to the body of tweets.
5/
As you can see... convoy twitter is a pretty mixed bag. The chart below shows the average sentiment scores for each topic. Another one you might want to download and look at in high-res.

6/
How to read the charts that follow:
- vertical axis + color indicate a token's importance to the topic
- horizontal axis + size show how frequently a term is used in the entire collection of tweets.

Now we'll delve into the more relevant topics in detail. 👇👇👇

7/
The most common topics in the model (66, 29, 31) involve the most abundant hashtags and a lot of right-populist style empty calorie rhetoric about freedom or tyranny.

8/
The bulk of tweets are devoid of almost any substance at all. Perhaps this confirms the analysis that populist style of politics (shout out @CamScottCPC) being mostly empty calorie signifiers. Much of it could have been plucked right from an early 2000s Tea Party rally.
6/
A large portion of pro-convoy tweets (maybe this is just twitter in general) are repetitive content-empty hashtag recycling. At least some of this stuff appears to be driven by likely bots, since it is spammed in odd patterns and at unusually rapid rates.

7/
It's not all substance free though. There are some more focused and coherent topics, including a few that are rather direct about their immediate (publicly stated, at least) goals. Nothing very surprising here. They want *all* mandates to end and Trudeau to go.
8/
There are several more Trudeau-centric topics. Honestly they give the Canadian Left a run for our money in hating this guy. Broken clocks and all that.
9/
There are several media related topics. Topics 51 and 58 on their intense dislike of the the main stream media. First of many references to "globalists" that seem to pop up everywhere🤔
10/
And fair enough, corporate media are in fact incredibly untrustworthy. What sources are they turning to? Canadian and US right-wing media are very well represented with Tucker Carlson on top of the pile. Jordan Peterson is there. And Elon Musk. Of course, why wouldn't he be?
11/
Another topic where Canadian/US right-wing media figures and politicians are very prominent. The main contenders here: Rebel News, Ezra Levant, and once again Jordie B. Ciderman. Far right political figures like Maxime Bernie and Randy Hillier also appear frequently.
12/
Topic 4: repetitive insistence that the convoy protests are peaceful. Also featured are hundreds of furious tweets on a (non-existent in reality) double standard in how movements are judged featuring racist tropes about BLM "burning down cities" and much worse than that
13/
Topic 2 is somewhat contested, covering the impact of the pandemic and convoy on people and society. Pro-convoy tweets almost exclusively talked about how mandates impacted individuals. Anti-convoy posters talked more about the pandemic and also harms caused by the convoy.
14/
In Topic 30, pro and anti convoy posters have it out more intensely. Perhaps a bit of projection as pro-convoy people reflexively throw back every (totally credible) accusation against them onto their opponents.
15/
Even though I tried to collect only pro-convoy related tweets, plenty of convoy-critical tweets still slipped through the filter. Topic 25 picks up most of the anti-convoy hashtags in the dataset.
16/
There's also a pretty sizable international contingent. First of all, in addition to all of the US right-wing media sources, there is enough MAGA leakage from the US to form a topic.
17/
Also represented are copycat convoy or anti-vaxx movements from The Netherlands (Topic 10); Italy (Topic 18); Australia (Topic 35); France (Topic 63) and more still.
18/
There were a few more mundane topics, like Topic 37 on fundraising or Topic 57 on convoy, protest, and occupation logistics. Also, there were more than a few that reflected the incoherent and chaotic nature of the convoy a bit too well to be coherent.
19/
I'll be updating this thread tomorrow with further review of some of the remaining topics... this is the point where things start to get a bit weird, even compared to what we have already seen.
20/
Round two, here we go! Time to wade into the more eccentric topics in pro-convoy twitter.

Topic 44: intense conspiracy mongering hashtags.
- Weird Nuremberg stuff ✅
- Mass psychosis pseudo-science ✅
- COVID deaths caused by vaccines ✅
- Great Reset conspiracy garbage ✅
21/
They are really fixated on the Great Reset conspiracy, it comes up over and over. Topic 27 features a lot of international pleas of solidarity with the "true north strong and free" and (for some reason) more great reset conspiracy stuff.
22/
Yet another Great Reset conspiracy topic, this time brought to us by the Brazilian anti-vax movement. The only mentions of billionaires in the entire dataset (80%+ spammed by a single user) occur within this highly conspiratorial, miles-away-from-class-analysis framing.
23/
For those who aren't aware, the Brazilian far-right gave the whole "astroturf trucker convoy for freedom" thing a trial run last year.
aljazeera.com/news/2021/9/9/…
24/
Topic 3: Uh... god bless Pat King and anti-vaxx doctors. Speaks for itself.
25/
Immediately following is a topic that is 86% (538/619) Tom Marazzo retweets by volume. An ex-cop and MBA holding business professor. Hard to imagine a more organic intellectual of the working class, right?
26/
Topic 17 is a real delight: the good vibes topic. They are all about love, peace, respect and unity. Sending hugs and smiles out into the world to defeat mandates. Find out the best part of topic 17 below 👇
27/
Amazingly, there are 18 tweets in this topic circulating a totally meaningless inspirational quote that is misattributed to fucking Che of all people.

"Don't forget, not when you lose, but when you but when you give up, you get defeated." - Che Guevara
👏👏👏
28/
People on both sides had a lot of really serious stuff to say about bouncy castles.
29/
Topic 50 is about 70% about the occupations and police. The rest? There are 170/599 (28%) tweets in this topic about... 💰SELLING MERCH💰. Pro-police t-shirts in this case. Pretty consistent with all of the merch booths/vans spotted at their carnivals of petty-bourg rage.
30/
Topic 42, the pseudoscience and "medical segregation" topic, is a worthy addition to the bunch. Hey look, Rebel News again! How did they get in there?
31/
Brian Denison, a fired anti-vaxx police officer that compared vaccine mandates to The Holocaust (while on the job, no less) basically gets his own topic (111/183 or 60% of topic tweets).
cbc.ca/news/canada/ca…
32/
Last, but not least we have Topic 22, which is founded on unhinged conspiracies about the pandemic itself being part of a nefarious, genocidal "new world order" conspiracy. Hmm, I wonder who they think is responsible? 🤔
33/
I don't think I can stand much more staring into the convoy twitter abyss. I'll post a detailed write up and further analysis on the convoy twitter dataset on the site.

While you're here, a quick reminder that defunding the police is a really great idea!

Oops... accidentally posted the wrong topic for this one. It's actually supposed to be topic 54. Editing tweets being behind a paywall is just one more strike against capitalism.

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More from @DataStruggle

Jul 13, 2021
Canadian capitalism's climate contradictions. Canada acts like a renegade petrostate when it comes to the #ClimateCrisis. But the energy sector is only a moderately sized and declining share of GDP, so why does it have such a dominant role in Canadian capitalism?

1/9
Since oil/gas production takes giant investments of capital, few jobs are created in the sector. Other than in Alberta, it's a really minor share of total employment. So the 'jobs, jobs, jobs' rhetoric they trot out to defend their climate mega-projects rings rather hollow.

2/9 Bar chart titled 'Oil and gas sector's oversized role in cap
Oil/gas plays an important role in filling Canadian capitalism's needs for new investment b/c of the sector's capital intensity.

That means that big corporate players in the industry now command huge amounts of capital, which confers considerable economic/political clout

3/9 Bar chart titled Oil and gas sector's undersized role in emp
Read 9 tweets
Jun 26, 2021
We should be wary of #UBI for so many reasons, it's hard to cover them all here. I will do a full length post on UBI in Canada and why we should stay far away from it soon.

To summarize (sources at end of thread):

1/10
In practice UBI will be used to destroy/commodify vital social programs. Left-wing proposals exist, but this is the form we're most likely get in reality. The one supported by right-wing economists, billionaires, and the drive of capital to commodify everything possible.

2/10
If UBI $$$ aren't generous enough to live on, it's a huge subsidy for low-wage employers, allowing them to pay marginalized workers sub-survival wages, especially those stuck in gig-economy hell. Think WalMart using food stamps to lower their wage bill times many billion.

3/10
Read 13 tweets

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