Yup, that's a big one. A topic where it is very noticeable is when it concerns monopolies.
Negative post or comment about monopolies when Americans are the online majority? Expect to get downvoted into oblivion.
Posted when Europeans are the online majority? Up you go.
To be clear: this isn't a complaint about downvoting, I'm just pointing out the phenomenon. For example, its possible to go to bed with +7 and wake up to -4 (a rather stark difference) because a certain comment was posted during the European evening, and thus was 'exposed' to American HNers longer than it was to European ones.
It would be nice to include an analysis of how good is this with real data. Perhaps pick all the submission from yesterday and show the correlation between the real points and the prediction.
The distribution of points is very 0 heavy. It can be a problem to represent it and to model it.
I didn’t read the code in the post and don’t have any deep familiarity with machine learning, but I have implemented a naive bayesian classifier to do something similar for tweets. The scores you get from that method don’t add up to 1 either.
This is somewhat interesting. If what is stated in the comment itself is true then that means the poster had to solve to find x and y such that 'This comment is going to collect the most votes yet is predicted to be Bad: x - Good: y' would give Bad: x and Good: y as the output when passed through the neural net. Maybe they also manipulated other parts of the comment to find x and y.
Would have been nice if some keywords were likely to increase or lower the score prediction. For instance, popular things like "rust" would probably increase popularity.
224 comments
[ 2.4 ms ] story [ 210 ms ] threadWell, this project doesn't offer this tool, but it tries.
Bad: 0.0598 - Good: 0.9307.
Bad: 0.9800 - Good: 0.0220
NB. Sorry for that, I just had to do it :)
Bad: 0.8083 - Good: 0.1910
"Scientists discover it is turtles all the way down" Bad: 0.9998 - Good: 0.0002
Bad: 0.9969 - Good: 0.0032
To be clear: this isn't a complaint about downvoting, I'm just pointing out the phenomenon. For example, its possible to go to bed with +7 and wake up to -4 (a rather stark difference) because a certain comment was posted during the European evening, and thus was 'exposed' to American HNers longer than it was to European ones.
"Show HN: Internet": Bad: 0.9987 - Good: 0.0013
The distribution of points is very 0 heavy. It can be a problem to represent it and to model it.
Bad: 0.0004 - Good: 0.9996
the history of X scores:
power: good 0.91
wealth: good 0.99
sex: good 0.99
squirrels: good 0.99
cables: bad 0.97
keys: good 0.99
why cables?
Maybe something to do with the wikileaks leaked cables?
>frosted butts
>Bad: 0.1214 - Good: 0.8659
I made cheese sandwiches for a week, here's what happened
And got: Bad: 0.0001 - Good: 0.9999
Not sure what to make of that.
Can you pass the butter?
But exactly the same score with:
Cannot your passed their butter?
Couldn't hill-climb past that.
t. Google employee knowing what do with my stocks
"Show HN: Announcing styled-components v5": 0.9973 Bad
So, don't use "Show HN"?
Bad: 0.9917 - Good: 0.0076
Good - 1.0 Bad. - 0.0
What do I win?
Each word added, "bitcoin", then "buy", then "now", drops the score lower until it hits 1.0 bad.
https://i.imgur.com/tJPp31G.png
"Steve Jobs is resurrected. Buy bitcoin"
Good 0.0, Bad 1.0
e.g: "Rihanna concert cancelled" Bad: 0.0006 - Good: 0.9992
vs "Rust 1.33 released" Bad: 0.9896 - Good: 0.0108
Bad: 0.0015 - Good: 0.9986
Not sure what I'm going to build, but it's going to make me a lot of money.
"A mouse killed our network engineer" - Bad: 0.2068 - Good: 0.7895
VS
"A rat killed our network engineer" - Bad: 0.9698 - Good: 0.0322
"A network engineer killed our mouse" - Bad: 0.0397 - Good: 0.9529