LongDraw
Really Really Experienced
- Joined
- Jan 12, 2022
- Posts
- 352
What I'm suggesting is relatively simple and is used to improve statistical accuracy by removing inaccurate results through collection of larger data sets. So basically it works like this:
Story A is published. Hour 1 there are three 5* votes, hour 2 there are four 1* votes, hour 3-5 there are ten 4* votes followed by another five 1* votes over next 3 hours.
So Story A would have a (64/22)/5 = 2.909/5 score.
Let's say over the course of the week, there are another 78 votes cast, majority 3* or 4*, with maybe another 5-10 votes being 1*. This should indicate 1* vote bombing occurring. So to correct the malicious voting, 30 days later if the story average score is 3.00-3.99, you remove all the 1* votes cast in prior month. If average score is 4.00-4.99, you remove all 1* & 2* votes from prior month.
Thoughts?
Story A is published. Hour 1 there are three 5* votes, hour 2 there are four 1* votes, hour 3-5 there are ten 4* votes followed by another five 1* votes over next 3 hours.
So Story A would have a (64/22)/5 = 2.909/5 score.
Let's say over the course of the week, there are another 78 votes cast, majority 3* or 4*, with maybe another 5-10 votes being 1*. This should indicate 1* vote bombing occurring. So to correct the malicious voting, 30 days later if the story average score is 3.00-3.99, you remove all the 1* votes cast in prior month. If average score is 4.00-4.99, you remove all 1* & 2* votes from prior month.
Thoughts?