Around 1 year ago we got hit badly on our [blogging platform][0] by people/groups submitting fake customer support description of other big companies, either being Microsoft, Facebook, Comcast etc. Rolled out a machine learning model and trained it on the database. 99% of them vanished. Next day, the machine didn't work and success rate was around 5%. Found out, they have learned the trick and now using symbols from…
How do you detect the ground truth for training the model? Do you manually label it?
Basically, pulled the database into CSV file and anything that was published before the bad content was classified as HAM.
We had content that were OK, so marked as HAM and then our new bad content all marked as SPAM.
When deployed to production for some hours HAM content got wrongly marked and model got trained on them as well which made so many confusion but the problem was taken care of once the model got properly tuned and safer to let it be automated.