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.

多参与者讨论倾向于以树状结构展开，预测分支并理解创建新分支的原因对于总结和线索解脱等下游任务非常重要，本文定义了分支预测的新任务，并提出了 GLOBS（全局分支评分）- 一种用于预测分支的深度神经网络模型。GLOBS 在 Reddit 的三个大型讨论论坛上进行了评估，取得了与一系列竞争基准线相比显著的改进，展示了更好的可迁移性。我们确认了在 GLOBS 成功中结构、时间和语言特征的贡献，并发现分支与更多的对话参与者相关，并倾向于发生在对话树的较早级别。我们公开发布了 GLOBS 及所有基准模型的实现，以便复现并推动对这一重要任务的进一步研究。