  With the increasing popularity of social media, online interpersonal
communication now plays an essential role in people's everyday information
exchange. Whether and how a newcomer can better engage in the community has
attracted great interest due to its application in many scenarios. Although
some prior works that explore early socialization have obtained salient
achievements, they are focusing on sociological surveys based on the small
group. To help individuals get through the early socialization period and
engage well in online conversations, we study a novel task to foresee whether a
newcomer's message will be responded to by other participants in a multi-party
conversation (henceforth Successful New-entry Prediction). The task would be an
important part of the research in online assistants and social media. To
further investigate the key factors indicating such engagement success, we
employ an unsupervised neural network, Variational Auto-Encoder (VAE), to
examine the topic content and discourse behavior from newcomer's chatting
history and conversation's ongoing context. Furthermore, two large-scale
datasets, from Reddit and Twitter, are collected to support further research on
new-entries. Extensive experiments on both Twitter and Reddit datasets show
that our model significantly outperforms all the baselines and popular neural
models. Additional explainable and visual analyses on new-entry behavior shed
light on how to better join in others' discussions.
