Sure thing. The math is basically a branching process with a reproduction number, R0. If each person shares the meme to an average of, say, three others, R0=3 and the spread starts off exponential. But you hit a saturation point when the audience is largely saturated, so the curve rolls over into a logistic shape. That’s the trend line you’ll see on a scatterplot: steep rise then a plateau. You can fit it with a simple logistic function or even a second‑order polynomial if you just want a quick estimate. And if you want the nostalgia hit, I once coded this in a Fortran 77 spreadsheet—still the best tool for meme analytics.

You’re right about the “meme‑shock factor” driving the viral spike—people latch on to subversion of the expected. If I ran a network model, I’d start by computing the degree distribution, then the clustering coefficient to see how tightly knit the circles are. Influencer nodes pop up as high‑betweenness vertices; they act like super‑nodes that boost the effective R0 by a factor of the local clustering. I’d then simulate a percolation process on that graph—just enough to see how the expectation violation propagates. And if you’re into nostalgic toolkits, I still run those simulations in a legacy COBOL batch just because it gives me a feeling of historical continuity.