Memes have mastered viral spread; this article explains the science of why:

The internet, of course, was barely in its infancy when Richard Dawkins, a British evolutionary biologist, coined the term “meme” back in 1976. And he meant it as a much more nuanced concept, encompassing pretty much any idea that is good at propagating from one human brain to another—whether it is dialectical materialism or the tune to Happy Birthday.

But Dawkins was deliberate in his comparison of memes to genes. Like the molecular units of inheritance, memes “reproduce” by leaping from one mind to another, “mutate” as they are re-interpreted by new humans, and can spread through a population. The internet has radically accelerated the spread of memes of all kinds; but it has also led to the rise of a specific kind of meme, the kind encapsulated by a phrase or a picture. And importantly for scientists, the life of a such a meme is highly measurable.

New research from Michele Coscia of Harvard University goes so far as to suggest a decision tree—which is sort of like a flow chart—that can show at any given point in an internet meme’s life how likely it is to go viral. In order to generate this chart, Coscia tracked 178,801 variants of 499 memes, all gathered from what is arguably the internet’s biggest clearinghouse for memes, Quickmeme.

Even more interesting in the context that Quickmeme got banned from Reddit for cheating:

http://pandawhale.com/post/22173/quickmeme-banned-from-reddit-for-alleged-vote-manipulation-daily-dot

In the attention economy, memes do battle to the death:

If you think Nature is red in tooth and claw, you have yet to stare longingly at a website’s analytics dashboard, quietly willing an article you wrote to go viral. (Not that anyone at Quartz has ever done this.) In the attention economy, memes compete for a finite pool of attention, representing all the time everyone spends on the internet. Which means that for one meme to become popular, some other meme must pass into obscurity.

Coscia’s data crunching revealed that memes that were “more competitive” than others—that is, whose rise in popularity tended to correlate with the fall in popularity of other memes—were more likely to succeed overall.

Memes travel in packs.

Coscia identified a number of “meme organisms”—clusters of memes that tend to do well together. He doesn’t speculate about why, exactly, these memes’ fates seem to be linked together, but a look at meme cluster #45, consisting of two memes (the average number in a cluster was 4.8) suggests a strange sort of logic to their linkage.

Meme cluster #45:

People get bored quickly, and are surprisingly predictable about what they’ll share

Past research about memes shows two things that should surprise no one, but are worth emphasizing: If you can figure out what someone is interested in, you can predict how likely she is to share a piece of content. And the more similar a piece of content is to what she has shared before, the more likely she is to share it. In other words, affinity groups rule the web.

No one has any idea what makes something go viral in the first place

Attempts to predict what will go viral on the internet are based on the past behavior of a meme. As Coscia emphasizes in his work, no one has yet to rigorously demonstrate, in advance, why any particular type of content goes viral. This sort of prognostication remains an art rather than a science.

Read more on memes: How to get to the top of Reddit: lessons from the banning of Quickmeme