Here’s something that’s likely to be one of the more specific predictions on Nieman Lab this year.

Sometime in the first few months of 2021, a social media user will share a picture of a newly released Moderna vaccine information packet distributed with the doses of the vaccine. The user will be shocked (shocked!) that it says the vaccine has not been evaluated for adverse effects on fertility. Which is weird, they’ll say — why wouldn’t they want to test that? Could this have to do with the whole syncytin-1 thing? What are they hiding? Is this evidence of a cover-up of history’s largest sterilization event — Exhibit A, right in the pamphlet?

This technique — which has been used so regularly by anti-vaccination activists that it has its own name, “argument by package insert” — will take people hours to debunk definitively. As shares click into the thousands, some fact-checker will write it up, patiently explaining that the timelines and ethical restrictions around most new drug trials end up producing little fertility data; the language in the pamphlet is boilerplate, not unique to this drug; and package insert language is regulatory disclosure, not a review of all research.

And when the original claim is, in fact, debunked, for the gazillionth time, the platforms will add a simple note to the posts sharing it. The note won’t say that this is a variation on a piece of disinfo older than Britney Spears’ “Toxic,” that it has always been used deceptively, and has been repeatedly found without merit, year after year, month after month, day after day.

It will say the claim is “disputed” and link to a specific fact-check. Then we’ll move onto something else — something equally predictable and equally specific.

Maybe it’ll be someone misframing documents about tracking doses as RFID-nanobot tracking in the vaccine itself. Or claims about disturbing vaccine outcomes in Britain that don’t account for recipient age. Or crisis actors, videos of people being “rounded up,” timeworn misrepresentations of VAERS or VAERS-like data. The sort of claims that a lot of people in the vaccine and political misinformation space could probably predict accurately, today, at any level of granularity desired. They’ll be seen as new claims, generate new debunks, and spark new debates about labeling or removal. And we’ll do it over and over again, as if each claim was a special snowflake.

In the mind of the public, disinformation is a series of endlessly creative and unpredictable attacks by unknown actors. In reality, much of what flies around is pretty predictable. It’s the same narratives, the same stories, the same techniques. It’s the same people spreading it, rotating in a limited number of new celebrities and plot twists each season. We likely already know most of the claims coming in the impending flood of Covid-19 vaccination misinfo. We know why those claims are wrong, or at least have historically turned out wrong. We know this because they’re likely to be the same claims on new hangers, ready-to-hand and minimally fitted to new events.

Yet too often, each claim is treated as a one-off — as if the history of the claim, tactic, or those spreading it can’t be taken into account. Platforms and web users are asked to make judgments, but they’re asked to do so in ways that often discard the most important information, at least early on: Does the person making or amplifying the claim have a history of making false statements? Is there a long history of similar claims being used to deceive? Students are encouraged to evaluate claims “on their own terms.” Platforms don’t provide historical or tactical context on likely deceptive claims while they’re being checked more deeply, even if they’re small variations on false claims seen a million times before.

We just keep plodding through the process.

I don’t claim to know what the exact remedies are here. But aside from the more narrow predictions in the introduction, I’d like to think that this is the year we all — from educators to platforms to users — make better use of the predictability of online misinformation and those who amplify it. More pre-bunking of the claims we know are coming, more indicators that various actors have a history of deception or error. More recognition that a claim that is a variation on repeated lies cannot be accorded the same initial epistemic status as claims that are truly novel. An education that focuses less on deep analysis of novel claims, and more on quickly finding the history and status of known claims and the reputation of those making them.

Recognizing that much misinformation is neither surprising nor novel may be demoralizing in a sense, but it might light the way to a more proactive approach. And maybe, just maybe, that will make the future less depressingly predictable.