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Quora is a popular Q&A site which provides users with the ability to tagquestions with multiple relevant topics which helps to attract quality answers.These topics are not predefined but user-defined conventions and it is not sorare to have multiple such conventions present in the Quora ecosystemdescribing exactly the same concept. In almost all such cases, users (or Quoramoderators) manually merge the topic pair into one of the either topics, thusselecting one of the competing conventions. An important application for thesite therefore is to identify such competing conventions early enough thatshould merge in future. In this paper, we propose a two-step approach thatuniquely combines the anomaly detection and the supervised classificationframeworks to predict whether two topics from among millions of topic pairs areindeed 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., thewinning convention, achieving an F-score of 0.898. Our system is also able topredict ~ 25% of the correct case of merges within the first month of the mergeand ~ 40% of the cases within a year. This is an encouraging result since Quorausers on average take 936 days to identify such a correct merge. Human judgmentexperiments show that our system is able to predict almost all the correctcases that humans can predict plus 37.24% correct cases which the humans arenot able to identify at all.

Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs