  We address the problem of maximizing user engagement with content (in the
form of like, reply, retweet, and retweet with comments)on the Twitter
platform. We formulate the engagement forecasting task as a multi-label
classification problem that captures choice behavior on an unsupervised
clustering of tweet-topics. We propose a neural network architecture that
incorporates user engagement history and predicts choice conditional on this
context. We study the impact of recommend-ing tweets on engagement outcomes by
solving an appropriately defined sweet optimization problem based on the
proposed model using a large dataset obtained from Twitter.
