Answers.Body: <p>If the only feature you're classifying on is the number of users making a given report, then this isn't really much to do with AI/ML. Just pick a number based on your subjective judgment and go with it.</p>
  
  <p>OTOH, if you can include details of the report itself (as well as the number of reporters), I think you might be able to build a bayesian classifier that would be useful.  If you could consider location, weather, time of day, number of reporters, etc., it seems like you might be able to get something useful put together.  </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-02-26T09:47:48.923Z
Answers.LastActivityDate: 2017-02-26T09:47:48.923Z
Answers.UserDisplayName: mindcrime
Answers.Body: <p>It's a trust-level problem, so your judgment is the best to decide what would be rhe threshold. </p>
  
  <p>You can help your decision making by trying and visualize how many accident (in %) you've left out... this can be an indicator of good threshold. You don't want to throw too many of them. But only you know what is good and bad in this case</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-02-11T07:00:18.877Z
Answers.LastActivityDate: 2017-02-11T07:00:18.877Z
Answers.UserDisplayName: Nassim Ben
Body: <p>I have users' reports about an accident. I want to know how to make sure that the number of reports is big enough to take that accident as a true accident and not spam.</p>
  
  <p>My idea is to consider a minimum number of reports in a specific time interval, for example 4 reports in 20 minutes are good enough to believe the existence of that accident.</p>
  
  <p>My question is how can I choose the minimum number of reports and that time interval? Is there some logic to make that decision?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-02-10T09:19:53.273Z
Comments.Text: This kind of question,should fit here [CrossValidated](http://stats.stackexchange.com/questions)
Comments.UserDisplayName: quintumnia
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-02-21T20:09:48.707Z
Comments.Text: Your problem is an outlier / unusual event detection problem. Countless methods exist for this. A threshold is very simple but, in my opinion doesn't reflect the reality. What if you have an accident alone, at night, on a small countryside road? These situations exist and discarding them per se is doesn't sound like a good approach to me... I agree Cross Validated SE would be more adapted.
Comments.UserDisplayName: Eskapp
Tags: <decision-theory>
Title: Making decision based on users' reports
UserDisplayName: honeyyy
ThreadText: # Making decision based on users' reports
  
  (Asked by: username_0 on 2017-02-10)
  
  I have users' reports about an accident. I want to know how to make sure that the number of reports is big enough to take that accident as a true accident and not spam.
  
  My idea is to consider a minimum number of reports in a specific time interval, for example 4 reports in 20 minutes are good enough to believe the existence of that accident.
  
  My question is how can I choose the minimum number of reports and that time interval? Is there some logic to make that decision?
  
  **Tags:** decision-theory
  
  > **Comments:**
  > **username_1**: This kind of question,should fit here [CrossValidated](http://stats.stackexchange.com/questions)  
  > **username_2**: Your problem is an outlier / unusual event detection problem. Countless methods exist for this. A threshold is very simple but, in my opinion doesn't reflect the reality. What if you have an accident alone, at night, on a small countryside road? These situations exist and discarding them per se is doesn't sound like a good approach to me... I agree Cross Validated SE would be more adapted.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2017-02-26. Score: 1
  
  If the only feature you're classifying on is the number of users making a given report, then this isn't really much to do with AI/ML. Just pick a number based on your subjective judgment and go with it.
  
  OTOH, if you can include details of the report itself (as well as the number of reporters), I think you might be able to build a bayesian classifier that would be useful. If you could consider location, weather, time of day, number of reporters, etc., it seems like you might be able to get something useful put together.
  
  
  ---
  
  ### Answer by username_4 on 2017-02-11. Score: 0
  
  It's a trust-level problem, so your judgment is the best to decide what would be rhe threshold.
  
  You can help your decision making by trying and visualize how many accident (in %) you've left out... this can be an indicator of good threshold. You don't want to throw too many of them. But only you know what is good and bad in this case
  
  
  ---
  
  