  Quora is a popular Q&A site which provides users with the ability to tag
questions with multiple relevant topics which helps to attract quality answers.
These topics are not predefined but user-defined conventions and it is not so
rare to have multiple such conventions present in the Quora ecosystem
describing exactly the same concept. In almost all such cases, users (or Quora
moderators) manually merge the topic pair into one of the either topics, thus
selecting one of the competing conventions. An important application for the
site therefore is to identify such competing conventions early enough that
should merge in future. In this paper, we propose a two-step approach that
uniquely combines the anomaly detection and the supervised classification
frameworks to predict whether two topics from among millions of topic pairs are
indeed competing conventions, and should merge, achieving an F-score of 0.711.
We also develop a model to predict the direction of the topic merge, i.e., the
winning convention, achieving an F-score of 0.898. Our system is also able to
predict ~ 25% of the correct case of merges within the first month of the merge
and ~ 40% of the cases within a year. This is an encouraging result since Quora
users on average take 936 days to identify such a correct merge. Human judgment
experiments show that our system is able to predict almost all the correct
cases that humans can predict plus 37.24% correct cases which the humans are
not able to identify at all.
