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.
A couple of days ago I got it into my head to upgrade one of my Exocortex servers from Ubuntu Server 14.04 LTS to 16.04 LTS, the latest stable release. While Ubuntu long-term support releases are good for a couple of years (14.04 LTS would be supported until at least 2020) I had some concerns about the packages themselves being too stale to run the later releases of much of my software. To be more specific, I could continue to hope that the Ruby and Python interpreters I have installed could be upgraded as necessary but at some point the core system libraries would be too old and they'd no longer compile. Not good for long-term planning.
First off, whenver you're about to do a major upgrade of anything, read the release notes so you know what you're getting yourself into. You'll also usually find some notes about all the new goodies you'll be able to play with.
In the past I've had nothing but trouble using the documented Ubuntu release upgrade process, so much so that I've had clients sign "I told you so," documents when they pressured me to do so because the procedure could reliably be expected to leave the system completely trashed, and a full rebuild was the only recourse. This time I set up a testbed in Virtualbox which consisted of a fully patched Ubuntu Server 14.04.5 LTS install. I ran through the documented upgrade process, and much to my surprise it went smoothly, leaving me with a functional virtual machine at the end of a 45 minute procedure (most of which was automatic, I only had to answer a few questions along the way). The process consisted of logging in as the root user (sudo -s) and running the updater (do-release-upgrade).
So, if it's so easy, why am I writing a blog post about it? Why worry?
Why worry, indeed. Read on.
First, you need someplace for the software to live. I'll say up front that you can happily run Huginn on your laptop, desktop workstation, or server so long as it's not running Windows. Huginn is developed under Linux; it might run under one of the BSDs but I've never tried. I don't know if it'll run as expected in MacOSX because I don't have a Mac. If you want to give Huginn a try but you run Windows, I suggest installing VirtualBox and build a quick virtual machine. I recommend sticking with the officially supported distributions and use the latest stable version of Ubuntu Server. At the risk of sounding self-serving, I also suggest using one of my open source Ubuntu hardening sets to lock down the security on your new VM all in one go. If you're feeling adventurous you can get a VPS from a hosting provider like Amazon's AWS or Linode. I run some of my stuff at Digital Ocean and I'm very pleased with their service. If you'd like to give Digital Ocean a try here's my referral link which will give you $10us of credit, and you are not obligated to continue using their service after it's used up. If I didn't like their service (both commercial and customer) that much I wouldn't bother passing it around.
As serious web apps go, Huginn's system requirements aren't very high so you can build a very functional instance without putting a lot of effort or money toward it. You can run Huginn in about one gigabyte of RAM and one CPU, with a relatively small amount of disk space (twenty gigabytes or so, a fairly small amount for servers these days). Digital Ocean's $10us/month droplet (one CPU, one gigabyte of RAM, and 30 gigabytes of storage) is sufficient for experimentation and light use. To really get serious usage out of Huginn you'll need about two gigabytes of RAM to fit multiple worker daemons into memory. I personally use the following specs for all of my Huginn virtual machines: At least two CPUs, 60 gigabytes of disk space, and at least four gigabytes of RAM. Chances are, any physical machine you have on your desk exceeds these requirements so don't worry too much about it (but see these special instructions if you plan on using an ultra-mini machine like the Raspberry Pi). If you build your own virtual machine, take into account these requirements.
Some time ago I was doing a longform series on Exocortex, my cognitive prosthetic system. I left off with some fairly broad and open-ended questions about the implications of such a software system for identity and agency. Before I go on, though, I think I'd better define some terms. Identity is one of those slippery concepts that you think you get until you have to actually talk about it. One possible definition is "the arbitrary boundry one draws between the self and another," or "I am me and you are you." A more technical definition might be "the condition or character as to who a person or what a thing is; the qualities, beliefs, et cetera that distinguish or identify a person or thing." That said, in this context I think that a useful working definition for the word 'identity' might consist of "the arbitrary boundry one draws between the self and another being that may or may not incorporate the integration of tools or other augmentations." Let us further modify the second, technical definition to include "the condition or character as to who a person or what a thing is or consists of due to the presence or absence of augmentations that modify the capabilities and/or attributes thereof," due to the fact that the definition should explicitly take into account the presence or absence of software or hardware augmentations. We also need to examine the definition of the word agency, which seems even more problematic. The Free Dictionary says that one definition is "the condition of being in action or operation," or loosely "being able to do stuff." The Stanford Encyclopedia of Philosophy says (among other things) the following about agency as a concept: The exercise or manifestation of the capacity to act. Of course, there are also arguments about the philosophy of agency that involve actors that should not be capable of having the intention to act doing so anyway, sometimes in ways that are functionally indistinguishable from organic life (which we usually think of as actors in the philosophical sense, anyway). And that's where things start getting tangled up.
Before I move on, I should set up two additional definitions. For the purposes of this post, 'agent' will refer to one of the functional units of Huginn used to construct solutions to larger problems. 'Constructs' will refer to the separate pieces of more complex software that plug into Huginn from outside.
The Internet Society has re-uploaded the video from my HOPE XI talk. Here it is:
Feel free to get a chuckle out of how nervous I am, but I hope you enjoy my talk, too.