Papers › Joint Effects of Context and User History for Predicting Online Conversation Re-entries
Joint Effects of Context and User History for Predicting Online Conversation Re-entries
Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections