Art: Pope Leo After Raphael, Fernando Botero, 1964
Curious about who he was, I clicked through to his profile, to see that he’d found me because he shared a picture of one of my stupid joke tweets in a meme group on Facebook.
By the way, I recently found out that you can do an advanced search Twitter for your top tweets, which gives me a chance to revisit some of my lamest classics:
Anyway, when I opened the group, I fell into a rabbit hole of re-shared, and some original, content from Twitter and Reddit. There was my own:
And there was other content, as well.
It made me wonder, how many of these groups were there? It turns out, a ton. There are Deep Learning Memes, Machine Learning Memes for Convolutional Teens (in a riff on the Dank Memes for Edgy Teens template), Scientific Methodposting, Computer Science Memes for Travelling Salesman Teens, and many, many more, all reposting content from Twitter, reddit, and generating their own meme content, which, in turn, gets posted to Twitter and Reddit.
“X Memes for Y Teens” is a trend which dates back to at least 2015. The most famous group was the UC Berkeley Memes for Teens group, which helped incoming Berkeley students connect with each other, and other American college groups, from where this stemmed:
UC Berkeley isn’t the only campus with a highly developed meme culture. Meme groups have become a mainstay of the United States’ elite universities, and at many schools, there are far more members than students. At Harvard, for example, there are almost six meme group members for every undergraduate.
Meme groups speak volumes about each school’s culture, perhaps even more than an admissions brochure could. Some prospective students join multiple pages to gauge which school would fit their personality and interests.
And, more generally, Facebook meme groups are not new, and in fact, some argue that they’re the only thing that’s still good about the gently smoldering landfill of human hopes and desires that is Facebook.
Scrolling through my feed has become a stressful, tiresome experience. But in the sea of very lukewarm takes and endless airport check-ins, there are a few beacons of light: spicy meme groups. In these private groups, memes go underground and get weird.
Incredibly niche meme groups are one of the few redeemable parts of the site — many of the groups, although closed, have thousands of members who all enjoy intensely specific content. Dubbed "Weird Facebook," the groups' absurdist humor and stupid jokes brought the social network back from its deathbed.
But what did interest me was the origin and recycling of meme content. Where do memes come from? How are they used, and reused? How much of the internet is actual, original content, and how much is, as the kids say, reposts?
Unfortunately, in the three minutes I spent googling this, I didn’t find any solid answers. However, there are people much more devoted to memeology than me, and they wrote an entire academic paper on the origin of memes in Reddit, Twitter, and a couple of other, fringe communities, and what they found was what most of us already know: memes are ephemeral, hard to explain, come from nowhere and vanish into the ether.
It’s REALLY hard to pin them down through measurable science.
The researchers gathered a database of more than 100 million images from online communities known to generate lots of memes, including Reddit, Twitter, 4chan, and Gab. They also downloaded more than 700,000 images from KnowYourMeme.com. Using perceptual hashing, or pHashing, they ran an algorithm through these image databases to detect visually similar memes. pHashing extracts a unique “thumbprint” from images, making it easier to detect patterns in visual similarity over a large database, and derive patterns out of those similarities. They then clustered similar memes by community, and studied how patterns evolved over time.
This research was specifically about political memes, but what I found most interesting was that, for something that is so intuitive and easy-to-understand for people in the know, it takes a LOT of math to try to relate memes to one another, and to try to tell political memes from non-political memes, to classify what a meme is, and so on.
Memes are hard to understand as a phenomenon, and almost impossible to track or monetize. Researchers have tried. Reddit has tried.
But now a 12-person team of Redditors (led by Brandon Wink and Ron Vaisman) is taking the idea behind r/MemeEconomy and making a working, interactive meme stock market. They’re calling the trading tool NASDANQ, a cheeky financial system for an alternate universe adjacent to our own where meme is king.
A meme economy doesn’t mean anyone with cash to burn will be able to gamble with Dat Boi shares on Wall Street. The market will operate on its own fictional currency — as on the subreddit, no one participating will actually use or make any real cash. Even without any dollars in play, the most important and difficult part of Wink and Vaisman’s project has been assigning a stock price to memes. “The idea is to give this usually intangible thing a value, so that people can feel like they’re earning something when before they could not,” Vaisman told The Verge. That means coming up with an algorithm capable of determining the value, based on a combination of popularity and growth, of every meme.
But no word on the success of this project - the latest news is from 2017.
There are a lot of features, as we say in the data science world, that make memes entirely unanalyzable, because you need a lot of judgement, intuition, sensitivity, and some element of being Extremely Online to tell what’s going on with memes. These are very squishy concepts that are hard to stuff into variables. All of this, to me, makes memes the ultimate, unfiltered expression of true humanity online.
Until now. Because memes have so much power, they’re being increasingly harnessed in marketing and politics. That’s just what happened in the academic paper, what’s happening with Brand Twitter, and also with (now) presidential candidate Michael Bloomberg a few days ago.
A bunch of huge Instagram meme accounts, including @FuckJerry and @Tank.Sinatra, posted sponcon Wednesday night for Democratic presidential candidate Michael Bloomberg. The memes, written in the form of fake DMs between Bloomberg and the accounts, playfully roast the billionaire candidate whose campaign leans heavily on online advertising.
@KaleSalad is a meme account run by BuzzFeed employee Samir Mezrahi. The company allowed him to post the sponsored content as a member of its Creators Program, which allows some non-news employees to monetize their own social media channels.
While BuzzFeed News employees are not permitted to express partisan political views or donate money or time to candidates, non-news employees are not bound to those rules.
"This deal was done between the campaign and Kale Salad exclusively — which is allowed under the guidelines of our Creators Program,” said BuzzFeed News spokesperson Matt Mittenthal. “The employee who oversees The Salad is not a News employee.” Mittenthal declined to comment on whether BuzzFeed would reconsider its policy of allowing non-news employees to accept money from political campaigns.
I came out of this article with more questions than answers: Why are Buzzfeed employees not allowed to monetize their social media challenges?
And, most importantly, how many other candidates are creating viral memes for an account called *checks notes* Kale Salad to distribute?
What will this new precedent mean for a format that’s currently all grassroots, impossible to track, impossible to analyze, and impossible to discover fakes in?
It’s unclear. I initially wanted to write a fun post about how much content is reshared online and recycled amongst Facebook and Twitter, but now, having researched this, I’m having some existential doubts about what memes are becoming. This can only mean that it’s time for me to get ahead of this to start my own Facebook meme group, Highly Questionable Memes for Normcore Teens.
What I’m reading lately:
“How do you talk to the I have nothing to hide people?”
New paper on the Python ecosystem:
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