How accurate is it?
Each question is assigned a score by the machine learning filter, and a lower score means the question is more likely to be bad. Different chat rooms have different thresholds; in SOCVR the threshold is 35, in SOBotics it's 45, and in FireAlarm Development it's whatever we want it to be at the moment.
At a threshold of 35, since the bot started running in SOCVR on February 8:
- 12% of reported posts have close votes but aren't closed or deleted,
- 13.98% are closed but not deleted, and
- 40.7% are deleted,
which adds up to 66.7%. That's pretty impressive, considering only about 9% of questions from that time period are closed. (I wasn't able to get statistics on deleted posts or posts with close votes.)
- 5.8% of non-deleted reported posts have a positive score
- 31.5% of non-deleted reported posts have a negative score
- 78.4% of all reported posts have a negative score, have close votes, are closed, or are deleted.
Where does it run?
Where's the code?
The code is on GitHub. FireAlarm uses two libraries I've helped write: SwiftChatSE, a Swift chatbot framework, and SwiftStack, an API wrapper FelixSFD and I have been working on. You may reuse any of the code in your own projects; it's all licensed under MIT.