1. People don't generally use the (big, whole-web-corpus-trained) general-purpose LLM base-models to generate bot slop for the web. Paying per API call to generate that kind of stuff would be far too expensive; it'd be like paying for eStamps to send spam email. Spambot developers use smaller open-source models, trained on much smaller corpuses, sized and quantized to generate text that's "just good enough" to pass muster. This creates a sampling bias in the word-associational "knowledge" the model is working from when generating.

2. Given how LLMs work, a prompt is a bias — they're one-and-the-same. You can't ask an LLM to write you a mystery novel without it somewhat adopting the writing quirks common to the particular mystery novels it has "read." Even the writing style you use in your prompt influences this bias. (It's common advice among "AI character" chatbot authors, to write the "character card" describing a character, in the style that you want the character speaking in, for exactly this reason.) Whatever prompt the developer uses, is going to bias the bot away from the statistical norm, toward the writing-style elements that exist within whatever hypersphere of association-space contains plausible completions of the prompt.

3. Bot authors do SEO too! They take the tf-idf metrics and keyword stuffing, and turn it into training data to fine-tune models, in effect creating "automated SEO experts" that write in the SEO-compatible style by default. (And in so doing, they introduce unintentional further bias, given that the SEO-optimized training dataset likely is not an otherwise-perfect representative sampling of writing style for the target language.)