Comment on Automatically hiding spam works

  1. da not following (Guest) Fri 24 Nov 2017 04:34PM UTC

    Slightly higher than 99% is a low percent?

    I think what's confusing me is the number 0,7%. The only percentage we know is: "99.3% accuracy rate when it comes to identifying spam works and comments". That remaining 0,7% =/= false positives, because it has to include false negatives as well, and it counts comment as well as works, so if you have 1000 legitimate works it's likely that less than 7 will be hidden, irregardless of the huge number of works being correctly marked as spam.

    I don't understand how you get "the system will hide 7 legitimate works for every spam work hidden". It's that ratio of 7 legitimate works hidden per 1 spam work hidden, when legitimate works hidden is the far smaller percentage. Wouldn't it be as the other are pointing out, something more like for every 7 legitimate works hidden, 7000 spam works are correctly hidden? That is, isn't the inverse true? (Maths are not my specialty, and googling false positive in relation to spam gave me articles like: https://www.networkworld.com/article/2327896/lan-wan/what-is-a-false-positive-.html which are for me hard to parse.)

    It's not great that some works (really, likely crack and people putting links to suspect websites in their work) will be hidden, it will be a low number, easily rectified, as opposed to the problem now of having humans slowly go through everything?

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    1. Foxglove87B Fri 24 Nov 2017 05:41PM UTC

      That number is not especially important, as it is the 1000 legit per 1 spam ratio, they are there only to show how even very favourable numbers can result in poor performance.

      I chose 0.7% because usually false positive ratio and false negative ratio are not too different. Maybe one is 5, 10 times the other, but rarely more.

      The rest of your comment shows a lack of knowledge in the field of statistics: it will be hard for you to understand the issue you're stuck on without conditional probability at the very least.

      Let's make it even simpler anyways.

      You have 10010 works. Of those works, 10 are spam, 10000 are legitimate.
      You have a very good spam system thay always catches spam works (0% false negative rate) and only mistakes 1 legit work for spam ofut of 100 legit works (1% false positive rate).
      Your system will find all 10 real spam works, and will confuse 1% of 10000 legit works as spam. 1% of 10000 is 100.
      In conclusion, your system will claim to have found 110 spam works, of which only 10 are spam: that means only 1 in 11 works marked as spam will be actual spam.

      The problem gets worse the more legit works there are for each spam work, and the higher the false positive rate.
      Clear enough?

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