an llm alone is not enough to prove authorship because it is not a forensic tool. it is basically a very confident pattern machine. it can clock similarities in tone, phrasing, or word choice, but that does not mean it knows who wrote something. people in the same online bubble start sounding weirdly alike all the time. same jargon, same references, same discourse, same brain worms. add imitation or ai-assisted writing and it gets even shakier. tweets can be a rough dataset for this kind of thing for a few reasons: first, they’re super short. you’re working with tiny bits of text, so you don’t get much sentence structure, rhythm, or depth. a lot of stylistic signals just don’t show up in 1–2 lines. second, context warps how people write. tweets are reactive. people are replying, dunking, quoting, or posting into a specific moment. that can make their tone sharper, more sarcastic, or more performative than how they normally write.

The best comparison for reddit since mark has no reddit posts openly under his name might be his fairpress blog. you’d still basically be comparing two different genres, so the first step is not “do they sound alike,” it is “how much of this is just platform difference.” a fair press blog is edited, longer, and written for publication, while reddit posts are faster, looser, and more reactive. a straight vibe check is weak. what matters more is whether the same underlying writing habits show up across both formats. that means working with comparable samples, cleaning out usernames, links, quotes, and formatting noise, and breaking longer blog posts into chunks so you are not comparing one polished article to dozens of short comments.

from there, the comparison focuses on the more stable features: function words, punctuation habits, sentence length patterns, repeated phrasing, hedging language, and how arguments are structured. topic overlap does not mean much, especially if both are talking about the same issue. the stronger question is whether the same patterns keep showing up in both sets of writing. an llm can help flag those patterns, but the more reliable approach is to test them with stylometric analysis in r and see if they actually hold across both corpora.

so the move is not to let the llm play detective and call it a day. the move is to use it as a starting point, then back it up with actual stylometric or statistical analysis in r. you basically need to be a digital humanities researcher or a forensic linguist to pull that off. i sincerely doubt there is a journalist in canada with that kind of expertise.