2019/06/04 by Xingshan Zeng, Zeng, Xingshan, Jing Li +6
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Opinion Dynamics and Social Influence #cs.CL
paper · pdf · doi:10.48550/arxiv.1906.01185
ACL 2019
arxiv created 2019/06/04 · openalex publication_date 2019/06/04 · arxiv updated 2019/06/05 · openalex created_date 2019/06/14 · openalex updated_date 2026/07/28
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.