Joint Effects of Context and User History for Predicting Online Conversation Re-entries
This addresses the challenge of enhancing user engagement in online platforms like Twitter and Reddit, but it is incremental as it builds on existing methods for re-entry prediction.
The paper tackles the problem of predicting whether a user will re-enter an online conversation they previously participated in, by proposing a neural framework that models conversation context and user history, achieving an F1 score of 61.1 on Twitter data and outperforming state-of-the-art methods.
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.