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With the increasing popularity of social media, online interpersonalcommunication now plays an essential role in people's everyday informationexchange. Whether and how a newcomer can better engage in the community hasattracted great interest due to its application in many scenarios. Althoughsome prior works that explore early socialization have obtained salientachievements, they are focusing on sociological surveys based on the smallgroup. To help individuals get through the early socialization period andengage well in online conversations, we study a novel task to foresee whether anewcomer's message will be responded to by other participants in a multi-partyconversation (henceforth Successful New-entry Prediction). The task would be animportant part of the research in online assistants and social media. Tofurther investigate the key factors indicating such engagement success, weemploy an unsupervised neural network, Variational Auto-Encoder (VAE), toexamine the topic content and discourse behavior from newcomer's chattinghistory and conversation's ongoing context. Furthermore, two large-scaledatasets, from Reddit and Twitter, are collected to support further research onnew-entries. Extensive experiments on both Twitter and Reddit datasets showthat our model significantly outperforms all the baselines and popular neuralmodels. Additional explainable and visual analyses on new-entry behavior shedlight on how to better join in others' discussions.

Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling