  Multi-participant discussions tend to unfold in a tree structure rather than
a chain structure. Branching may occur for multiple reasons -- from the
asynchronous nature of online platforms to a conscious decision by an
interlocutor to disengage with part of the conversation. Predicting branching
and understanding the reasons for creating new branches is important for many
downstream tasks such as summarization and thread disentanglement and may help
develop online spaces that encourage users to engage in online discussions in
more meaningful ways. In this work, we define the novel task of branch
prediction and propose GLOBS (Global Branching Score) -- a deep neural network
model for predicting branching. GLOBS is evaluated on three large discussion
forums from Reddit, achieving significant improvements over an array of
competitive baselines and demonstrating better transferability. We affirm that
structural, temporal, and linguistic features contribute to GLOBS success and
find that branching is associated with a greater number of conversation
participants and tends to occur in earlier levels of the conversation tree. We
publicly release GLOBS and our implementation of all baseline models to allow
reproducibility and promote further research on this important task.
