If “different typos” averaged out to “nearly no typos” the same logic would have different words average out to nearly no words.

No, because typos are irregular, so combining multiple sources does not reinforce them, whereas "words" (tokens would be a better term, because they're not always full words) tend to be used in similar ways, reinforcing those patterns. As you say, context is relevant, an LLM isn't just looking at the last token to decide the next, but at a much larger window. That does allow it to adjust to tone, as the probabilities of certain tokens, and so words, will depend on that tone, and the type of words used, and thus context, of a conversation. If emojis are used a lot in certain contexts, those patterns will tend to be reinforced in their training, and so produced more in their output.

As to filtering their input, at no point did I say they ingest "the entire internet", so quoting it seems rather disingenuous. They scrape as much text as they can get, both online, and by OCRing books, as we've seen with the recent upset about the number they destroy. What the commercial models do with this afterwards is uncertain, as anything they say is likely to be misleading for commercial purposes. I think it's a fair assumption that they want good quality data, however they define that, but filtering it all manually is obviously much too vast a project to do entirely manually, so it's done heuristically, which has the obvious problem that it'll let through low quality sources some of the time, lowering the quality of the overall data set. You only need to read the anodyne screed they produce to see how all of the little quirks and nuance that marks human communication tends to get left out, leaving LLM prose feeling rather vacuous and repetitive.