Tue. May 30th, 2023

What causes one meme to take the internet by storm, dominating image boards and inspiring hundreds of variations, while another languishes?

It’s a tantalizing question in the burgeoning field of meme theory, and not just because the answer could shed light on our collective online subconscious. It’s also possible that research into it could eventually explain broader aspects of cultural consumption—why an entire work, perhaps even a novel or a painting, is imitated, flopped, or ultimately forgotten.

Until now, most of the research into why a meme goes viral has focused on how its current position in a social network can be used to predict how the meme will continue to spread. The idea is that if you look at how influential the people who have already shared it are, their relationships with other people, and whether they shared it at a time when others are likely to see it, you can crunch the numbers and make an educated guess whether it will continue to spread or disappear.

The problem with that approach, according to Michele Coscia, a postdoctoral researcher who studies complex networks at the Harvard Center for International Development, is that the content of the meme itself is ignored. After all, an influential person can create a new meme and share it at the optimal hour and day of the week, but if it’s boring, it’s probably still a dud. It’s not that he sees the extrinsic meme theory as wrong-minded, he says; he just wants to look at the other side of the coin.

“There also has to be some value in the content itself,” Coscia said during a Skype interview.

Technically, a meme is any single unit of cultural exchange, and it can refer to anything from a chain letter to the “leap the shark” trope. To begin with, however, Coscia chose to study just one type of meme, and one of the most visible today: image macros, in which internet comedians talk about current events in bold white text or running jokes that appear over images known as Scumbag Steve. , Forever Alone and many others. His hypothesis was that the more a meme resembled an existing fare, the less likely it was to find viral success. like you
think about it though, both possibilities are tempting: new ideas are more memorable, but Foul Bachelorette Frog spawned Foul Bachelorette Frog, and so on.

“I think there’s support for Michele’s idea,” said Eytan Adar, a professor of information and computer science at the University of Michigan who studies online information flow. “There is certainly a pressure and incentive in social media to show that you knew or said something first. Memes that are very similar to something said before are likely to be treated as “old.”

First, Coscia needed a data set. He used the Meme Generator API to look at memes shared in the summer of 2013, analyzing the name of each meme to see if it was related to an existing meme, such as Socially Awkward Penguin and Socially Awesome Penguin. He then used the image analysis tool SURF to detect which images were similar, eventually breaking out the actual text of each meme variation to find out which spoke about the same current events.

Then Coscia used that data to create a massive map. On this, each node represents a meme. The larger the node, the more variations it spawned; the closer to orange it looks, the more votes it got on Meme Generator. Lines connect memes that Coscia’s analysis showed to be related – so Socially Awkward Penguin is connected to Socially Awesome Penguin, and both are connected to Socially Awkward Awesome Penguin.

The center of the card is a jumble of interrelated jokes and references. Towards the periphery, however, it develops into longer chains: series of hitchhiking memes less closely related to the groupthink in the middle. And it’s in that perimeter, outside of space where the memes are most similar, where the big orange nodes – the superstar memes that have generated many popular variations – are most likely to fall.

That suggests that Coscia’s hypothesis is correct. Similarity to other memes decreases the chance that a meme will be successful. Coscia, who first became interested in memes after reading Richard Dawkins’ 1976 “The Selfish Gene” and lurking on meme-centric subreddits, believes his research is the first to suggest that finding.

It shows “that the intrinsic features of memes and their similarity to each other are related to their likelihood of going viral,” Coscia wrote in a paper about the work that has since been published in Scientific Reports, an open access journal published by Nature. Publishing Group. “This is a remarkable result: it allows researchers to detect meme characteristics and use them to objectively explain why a meme is popular.”

“If you’re very similar to what’s already out there, I can guarantee you won’t be successful,” Coscia said. But “inequality does not necessarily mean success. There is no magic potion that tells you whether you will be successful or not.”

Of course, there’s no reason yet to assume that Coscia’s results apply to anything other than image macros. While he’s tight-lipped about what he’s working on now, he says he’s now working on a much larger data set — and that his long-term goal is to study how the structure of very complex works, such as art or literature, affect the way they spread.

It’s tempting to think of Coscia’s research as a guide to making successful memes, but it’s more useful, he says, as a guide to what not to do: If you want to be successful, don’t jump on a train.

You will never hear me recite guru-like advice to achieve success, such as ‘be different,'” he wrote on his blog when the paper came out. “That’s just bullshit.”

By akfire1

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