That is an overly simplistic problem description.<p>It&#x27;s trying to identify spam&#x2F;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.<p>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, <a href="http:&#x2F;&#x2F;www.datanami.com&#x2F;2014&#x2F;07&#x2F;17&#x2F;inside-sibyl-googles-massively-parallel-machine-learning-platform&#x2F;" rel="nofollow">http:&#x2F;&#x2F;www.datanami.com&#x2F;2014&#x2F;07&#x2F;17&#x2F;inside-sibyl-googles-mass...</a>