Jun 07 2020
Before I repost this Twitter thread in toto, I'd like to say a few things. First, Zander is an old friend of mine (pushing 20 years at this point). Second, while he might bill himself as "an amateur chemist," his scientific expertise has been helpful to me numerous times over the years, so I feel that I can vouch for his knowledge as well as his assessment of the situation. I asked him if I could repost this research earlier and he gave his permission. For clarity I've made minor edits to add punctuation. I've also reposted the images he used and gotten hold of copies of his references. I feel that, right now being able to verify the provenance of information is very important because information sabotage is an effective tactic.
Take it away, Zander.
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I’ve been seeing a lot of conflicting information about tear gas online and wanted to make a guide about what tear gas is, how it’s used and how to treat exposure to it. I have a background in experimental methodology and research, and am an amateur chemist.
Special thanks to @3liza for this thread that gave me inroads to the literature, @taliabear for proofreading this and my professional chemist friend who wishes to remain anonymous for reviewing this.
So the first thing to really drive home: Tear gas isn’t just one thing. There’s nearly a dozen different compounds that are used as riot control agents across the world. I’ll be focusing on the USA because that’s where I live.
Dec 18 2016
You've already read my opinion of the 2016 election's outcome so I'll not subject you to it again. However, I would like to talk about some weird stuff I (we, really) kept noticing on Twitter in the days and weeks leading up to Election Day.
As I've often spoken of in the past, a nontrivial portion of my Exocortex is tasked with monitoring global activity on Twitter by hooking into the back-end API service and pulling raw data out to analyze. Those agents fire on a stagged schedule, anywhere from every 30 minutes to every two hours; a couple of dozen follow specific accounts while others use the public streaming API and grab large samples of every tweet that hits Twitter around the world.
If you want to look at a simplified version of that agent network to see how it works I've made it available on Github. As you can see, the output of that particular agent network is batched into e-mails of arbitrary size using the Email Digest Agent and is sent to one of my e-mail addresses as a single batch. The reason for this is twofold; it's easier to scan through a large e-mail and look for patterns visually than it is to scan through several dozen to several hundred separate messages in sequence, and it uses fewer system resources on my e-mail provider to store and present to me that output.
Six or seven weeks before Election Day, Lifeline (the recognition code for the agent network which carries out these sorts of tasks for me) started sending me gigantic e-mail digests every hour or so, containing something like several hundred tweets at a time (the biggest was nearly a thousand, as I recall). Scanning through those e-mails showed that most of the tweets were largely identical, save for the @username that sent them. Tweets about CNN and the Washington Post being GRU and SVR disinformation projects or on-the-ground reporting tagged with #fakenews. Links pointing to Infowars articles (the tweets consisted of the titles of posts, links, and the same sets of hashtags; if you ran the Twitter-compressed URLs through a URL unshortener they all pointed to the same posts). Anti-Bernie and anti-Hillary tweets that all had the same content and the same hashtags. Trump as the second coming messages and calls to action. Rivers of bile directed at political comentators and reporters. Links to fake Wikileaks Podesta e-mails that went to Pastebin or other post-and-forget sites (there wasn't even enough data in the fakes to attempt to validate them (by the bye, the method linked to is really easy to automate)). I saw the same phenomenon with #pizzagate tweets, only the posts came in shorter bursts more irregularly. It went on and on, day and night for weeks, hundreds upon hundreds of unique copies of the same text from hundreds of different accounts. I had to throw more CPUs at Exocortex to keep up with the flood.
All of these posts, when taken together as groups or families consisted of exactly the same text each and every time, though the t.co URLs were different (a brief digression: Twitter's URL shortening service seems to generate different outputs for the same input URL to implement statistics gathering and user tracking as part of its business strategy). Additionally, all of those posts went up more or less within the same minute. The Twitter API doesn't let you pull the IP addresses tweets were sent from but the timestamps are available to the second. If you looked at the source field of each tweet (you'll need to scroll down a bit), they were all largely the same, usually empty (""), with a few minor exceptions here and there. The activity pattern strongly suggests that bots were used to strafe circles of human-controlled accounts on Twitter that roughly correspond to memetic communities. Figuring that somebody had already done some kind of visualization analysis (which I suck at), I had Argus (one of my web search bots) do some digging and he found a bunch of pages like this study, which seem to back up my observations.
The sort of horsepower needed to create such an army of bots would be very easy to assemble: Buy a bunch of virtual machines on Amazon's EC2. Write a couple of bots using Ruby or Python. Sign up for a bunch of Twitter accounts or just buy them in bulk. Make a Docker image that'll effectively turn one EC2 instance into as many as you can reasonably run without crashing the VM. Deploy lots and lots of copies of your bots into those Docker containers. Use an orchestration mechanism like Ansible to configure the bots with API keys and command them en masse; if you're in a time crunch you could even use something like pssh to fire them all up with a single command. Turn them loose. If you've been in IT for a year, this is a Saturday afternoon project that won't cost you a whole lot, but could make you a lot of money.
"Well, yeah, there was an army of bots advertising on Twitter. What else is new?" you're probably saying.
What I am saying is simply this: This post describes a little bit about how this sort of media strategy works, what the patterns look like at the 50000 foot view, and my/our observations. I don't think I did anything really ground-breaking here, only in the sense that I used a bunch of AI systems that stumbled across what was going on by accident. It was the hardcore data scientists who did the real academic work on it (though that work is a bit inaccessible unless you're a computer geek).
Memetic warfare is here, and our social networks at the battlegrounds. Armor up.