Joint Effects of Context and User History for Predicting Online Conversation Re-entries

Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong


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 bi-attention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-of-the-art methods from previous work.
Anthology ID:
P19-1270
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2809–2818
Language:
URL:
https://www.aclweb.org/anthology/P19-1270
DOI:
10.18653/v1/P19-1270
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1270.pdf
Video:
 https://vimeo.com/384738763