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Wow. This gives a lot of false positives, but it found all ~10 of my old accounts over the years. The most interesting thing is that my writing style changed p
by sillysaurusx 4y ago
Wow. This gives a lot of false positives, but it found all ~10 of my old accounts over the years.
The most interesting thing is that my writing style changed pretty drastically since a decade ago. Searching for my oldest account matches my earliest usernames, whereas searching this account matched the rest.
The details of the algorithm are fascinating: https://stylometry.net/about https://stylometry.net/about Mostly because of how simple it is. I assumed it would measure word embeddings against a trained ML model, but nothing so fancy.
- FormerBandmate 4y ago> sillysaurus3 > sillysaurus2 Tbf a human could have found a bunch of them relatively easily
- echelon 4y agoIt works like a charm for me too. I put in my username and found my pre-echelon alt, possibilistic. (Echelon was taken when I registered possibilistic, but it must have been unused and dropped.)
- costco 4y agoYeah top 20 is a little excessive because in my own tests I found that top 20 is only marginally more accurate than top 10. You can get a more academic explanation [here](https://www.tandfonline.com/doi/abs/10.1080/09296174.2011.533591?journalCode=njql20 https://www.tandfonline.com/doi/abs/10.1080/09296174.2011.53...). I was amazed too because it seemed too easy!
- sillysaurusx 4y agoFWIW, top 20 was necessary for mine. The bolding was a brilliant move. Several of my accounts were ranked 10-20, but popped out due to the bolding.
- justusthane 4y agoWhat does the bolding indicate?
- sillysaurusx 4y agoThe explanation is here: https://news.ycombinator.com/item?id=33755466 https://news.ycombinator.com/item?id=33755466 As far as I’m concerned, it’s the killer feature of the app. The top 20 results may be noisy, but the bolded results have a signal to noise ratio close to infinity.
- jsnell 4y agoThe precision of the bolded results looks like maybe 30% to me. Significantly better than the non-bolded, but nowhere near perfect precision.
- costco 4y agoFalse positives become an increasingly difficult problem the more and more potential authors you introduce. If I had wrote a fancier model it probably wouldn't be as much of a problem but what can you do.
- jsnell 4y agoYes, this wasn't a criticism of the tool. It is crazy good. But I don't think people should be making the assumption that bolded results are definite alts, which sillysaurus' comment reads like.
- sillysaurusx 4y agoHmm, that wasn’t my intent. I see this tool as a recommendation engine more than a doxxer. By “signal to noise ratio close to infinity,” I meant that if you visit one of the bolded accounts, they’ll probably sound a lot like you. It’s one of those ideas that makes the tool substantially more effective, yet never would’ve occurred to me. It’s like the simplicity of pg’s “a plan for spam” algorithm: deceptively simple, but (like scrubbing dishes with fingers) works really well.
- User23 4y agoI’d figured it would be some kind of n-gram frequency analysis. Would be interesting to code that up and compare.
- costco 4y agoIt is. The description on the about page is a little simplified but I basically I look at the most common word and character ngrams of size 1,2,3 (200 each), put all the frequencies in an array and then compare to all the other users with https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances_chunked.html https://scikit-learn.org/stable/modules/generated/sklearn.me....
- User23 4y agoCool, I only skimmed the description maybe I needed to read it more carefully. Have you considered doing rune rather than word ngrams? I can imagine that might be prohibitively expensive, but I really don’t know. I did something like that long long ago in C for automatic document language detection. It was quite accurate.
- deleted 4y ago[deleted]
- lettergram 4y agoFrankly similar to how I was doing in back in 2018 (when you and I chatted about it on HN lol) https://news.ycombinator.com/item?id=17944293 https://news.ycombinator.com/item?id=17944293 The approach I took was a bit different, but also no ML required. The real trick is pruning and going cross platform. There are around 100k active HN accounts (meaning posts a few times a year), maybe 200k if you count at least one post a year. But <10k that post weekly. It’s a very small space to try to compare so simple methods will work fine.
- costco 4y agoExactly. HN emphasizes long-form posts much more than other forums which makes the commenters here very susceptible to this kind of analysis. Plus you can fit every single HN comment in RAM on a mid tier gaming laptop so it's even easier. I was trying to think of applications of this kind of data and the only thing I could think of was moderation tools/detecting ban evaders but what you've done seems much more profitable lol.
- hnburnerUixoHr5 4y agoWoof. I create new accounts on a semi-regular basis because I think cliques are the most corrosive factor to social media. Any time my account gathers enough upvotes enough I destroy it for another. I had four accounts. None are over 50% confidence, but when I look at any one account the others are consistently #2, #3, and #4. Now I’m thinking very carefully about what words I use to avoid linking this as the 5th account.
- dimmke 4y agoOn the other side of the coin, I have never had an alternate HN account (beyond maybe 1-2 throwaways with only one post or comment) so seeing the list of users that are most similar to me was interesting. I didn't see some stark similarities based on a quick peek at their comments, but it was interesting.
- bb88 4y agosillysaurus3 was in mine. :) Clearly we're not the same.