That's a great background paper on the Netflix attack, we make a pretty direct comparison in section 5. We also try to use similar methods for comparison in sections 4 and 6. In section 5 we transform peoples Reddit comments into movie reviews with an LLM and then see if LLMs are better than naraynan purely on movie reviews. LLMs are still much better (getting about 8% but the average person only had 2.5 movies and 48% only shared one movie, so very difficult to match)
reply>we make a pretty direct comparison in section 5
replyawesome, i saw the mention in the introduction but i havent yet had a chance for a thorough read through of the paper -- ive just skimmed it. looking forward to reading it in-depth!
Throwaway accounts using "clever" turns of phrase can often be anonymized by double click, right-clicking -> googling their witty pun and seeing their the sole instance elsewhere, on Twitter, Facebook, etc
replyIf I see a couple words I dont know in a row, I can infer a posters real name.
Id be more specific but any example is doxxing, literally so
If you have access to the whole site dataset it's much more reliable with simpler checks. You can just use word usage frequency of common words. Someone posted a demo here of doing this to HN comments which was very effective at showing alt accounts for a user.
replyI assume one's vocabulary is basically a fingerprint, even if one doesn't use unique turns of phrase. Domain knowledge just leaks in and we aren't conscious of it being identifiable.
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