Researchers at MIT say they've found an algorithm that can tell
what's going to be trending on Twitter hours before it happens,
opening the door to modeling other kinds of data. Twitter's
trending topics — the ones shown on its home page — are selected
automatically, based on total number of tweets and a jump in how
often a topic is mentioned. But Associate Professor Devavrat
Shah and student Stanislav Nikolov say that by building a
statistical model based on a sample set of 400 topics, they can
tell with 95 percent accuracy whether or not a topic will end up
trending. In tests, topics can be predicted an average of an
hour and a half before they trend, with some predicted up to
four or five hours beforehand.  95 percent accuracy, an average
of an hour and a half beforehandWhile this development could be
used to optimize advertising linked to topics on Twitter, Shah
also thinks it could also be used to predict other things that
update at regular intervals. Unlike machine learning that looks
for different para