Not enough users browse the https://news.ycombinator.com/newest queue, there's 2 submissions per minute. I've seen articles ignored and then suddenly the third or fifth time it hits number 1 on the frontpage. It's much more random than you might expect.
I have statistical models that can predict if a headline will get > 10 votes or if a headline that gets > 10 votes will get a comments/votes ratio > 0.5. (We can’t think of a better headline than “Richard Stallman is dead”)
Both of those are treated as probability calibrated classifiers because of the extreme variations in outcomes. You might post something one day and get 0 upvotes, another day somebody might post it and get 500. Not that many people read the “new” page so it is really iffy if you get your first few votes.
In principle you could get more information out of the data treating it as a regression problem (e.g. give more points for predicting that an article gets 500 votes as opposed to 50) but one model I made never predicted that anything gets more than 16.7 votes which means the error function is going to always look awful and not really be a good tool for improving the model. The logit models I”m using, however, give realistic scores in the face of uncertainty.
If you wrote 10 of those one would be a hit, I believe that. I think "code reviews should be converging" has good potential for a discussion. My model looks at the headline only and does not consider the quality of the post or any metrics thereof. I thought the article was basically right but the use of the word "converging" is unconventional.
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Not enough users browse the https://news.ycombinator.com/newest queue, there's 2 submissions per minute. I've seen articles ignored and then suddenly the third or fifth time it hits number 1 on the frontpage. It's much more random than you might expect.
I have statistical models that can predict if a headline will get > 10 votes or if a headline that gets > 10 votes will get a comments/votes ratio > 0.5. (We can’t think of a better headline than “Richard Stallman is dead”)
Both of those are treated as probability calibrated classifiers because of the extreme variations in outcomes. You might post something one day and get 0 upvotes, another day somebody might post it and get 500. Not that many people read the “new” page so it is really iffy if you get your first few votes.
In principle you could get more information out of the data treating it as a regression problem (e.g. give more points for predicting that an article gets 500 votes as opposed to 50) but one model I made never predicted that anything gets more than 16.7 votes which means the error function is going to always look awful and not really be a good tool for improving the model. The logit models I”m using, however, give realistic scores in the face of uncertainty.
How does that model rank the title I shared?
8% likely to get >10 votes, 13% likely to get a big discussion, 23% likely to be "dead" but my "dead" model has problems.
interesting, can you run it on my blog? i bet it would generate false negative for you.
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I see If you wrote 10 of those one would be a hit, I believe that. I think "code reviews should be converging" has good potential for a discussion. My model looks at the headline only and does not consider the quality of the post or any metrics thereof. I thought the article was basically right but the use of the word "converging" is unconventional.so re-submitting after a couple of hours is really the way to go?
Maybe once at most. Re-submitting more than once within a few days will likely get it flagged by users.
Real SEO masters overcome this uncertainty by blogging more and recruiting other people to make links.