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A regression model trained from existing spam and abuse report data to twitter would cover ~99% of terrible at-replies on twitter. Look how effectively a hacke
by bishnu 11y ago
A regression model trained from existing spam and abuse report data to twitter would cover ~99% of terrible at-replies on twitter.
Look how effectively a hacked-together script that just uses a couple of heuristics like GGAutoBlocker works. [1]
Something a little more principled, trained with unfettered access to user data, would effectively kill the problem. IMO.
[1] https://github.com/freebsdgirl/ggautoblocker https://github.com/freebsdgirl/ggautoblocker
- harryh 11y agoYour understanding of what is difficult is wrong because you're looking at it as a detection problem when it's actually a false positive problem. See: http://laughingmeme.org/2011/07/23/cost-of-false-positives/ http://laughingmeme.org/2011/07/23/cost-of-false-positives/
- bishnu 11y agoThat is an overly simplistic problem description. It's trying to identify spam/abuse in a universal way, which is a much harder (and barely coherent) problem, and then delete those tweets which makes the cost of a false positive much higher. What’s far more feasible is to compute the probability a given user would like to see a specific tweet, and then have those below a certain threshold hidden (IE, don’t ping the notification tab for it). This is actually a very well-studied problem, and the massively parallel ML infrastructure it would take exists. Eg, http://www.datanami.com/2014/07/17/inside-sibyl-googles-massively-parallel-machine-learning-platform/ http://www.datanami.com/2014/07/17/inside-sibyl-googles-mass...