  As the online world continues its exponential growth, interpersonal
communication has come to play an increasingly central role in opinion
formation and change. In order to help users better engage with each other
online, we study a challenging problem of re-entry prediction foreseeing
whether a user will come back to a conversation they once participated in. We
hypothesize that both the context of the ongoing conversations and the users'
previous chatting history will affect their continued interests in future
engagement. Specifically, we propose a neural framework with three main layers,
each modeling context, user history, and interactions between them, to explore
how the conversation context and user chatting history jointly result in their
re-entry behavior. We experiment with two large-scale datasets collected from
Twitter and Reddit. Results show that our proposed framework with bi-attention
achieves an F1 score of 61.1 on Twitter conversations, outperforming the
state-of-the-art methods from previous work.
