Joint Effects of Context and User History
for Predicting Online Conversation Re-entries
Abstract
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 biattention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-ofthe-art methods from previous work