Predicting continuity of online conversations on Reddit

Abstract

Online conversation has been facilitating human society, which has encouraged people not only to share news, products, or daily events, but also to develop ideas. A popular type of online conversation is threaded conversation, where a person initiates a conversation with a new topic by uploading a post, then others reply to the post or the comments made by other participants in a recursive manner. Even though the growing importance of understanding and predicting the development process of threaded conversations has attracted research community to investigate continuity of online threaded conversations from a perspective of individual posts or comments, little work has focused on predicting the continuity or persistence of online conversations. In this paper, we propose a deep learning model to predict continuity of threaded conversations – whether there will be a newly-arrived comment or not. Cooperating with popular pre-trained text embedding models and graph neural network models, the proposed model captures text, structural, and temporal characteristics of the threaded conversation by an observable time for the final prediction. We evaluate the proposed model with two different types of the threaded conversations, self-introductory Q&A and discussion, which demonstrates that the proposed model can accurately predict the continuity of the conversations regardless of types of threaded conversations. We believe that the proposed methodology and the results can provide the potential insights to advertisers, opinion leaders, or platform designers who want to understand and predict the evolutionary process of threaded conversations. © 2023 Elsevier Ltd