  This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or quote interactions between a pair of accounts? Also, how
important is time locality, and which features of interaction patterns are most
important to enable accurate prediction of specific Twitter interactions? Our
empirical study of Twitter interactions contributes initial answers to these
questions.
  We have crawled an extensive dataset of Greek-speaking Twitter accounts and
their follow, quote, retweet, reply interactions over a period of a month.
  We find we can accurately predict many interactions of Twitter users.
Interestingly, the most predictive features vary with the user profiles, and
are not the same across all users.
  For example, for a pair of users that interact with a large number of other
Twitter users, we find that certain "higher-dimensional" triads, i.e., triads
that involve multiple types of interactions, are very informative, whereas for
less active Twitter users, certain in-degrees and out-degrees play a major
role. Finally, we provide various other insights on Twitter user behavior.
  Our code and data are available at https://github.com/twittermancer/.
  Keywords: Graph mining, machine learning, social media, social networks
