Conversation Modeling on Reddit Using a Graph-Structured LSTM

Victoria Zayats, Mari Ostendorf


Abstract
This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and temporal conversation structure. In experiments with a task of predicting popularity of comments in Reddit discussions, the proposed model outperforms a node-independent architecture for different sets of input features. Analyses show a benefit to the model over the full course of the discussion, improving detection in both early and late stages. Further, the use of language cues with the bidirectional tree state updates helps with identifying controversial comments.
Anthology ID:
Q18-1009
Volume:
Transactions of the Association for Computational Linguistics, Volume 6
Month:
Year:
2018
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
121–132
Language:
URL:
https://www.aclweb.org/anthology/Q18-1009
DOI:
10.1162/tacl_a_00009
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/Q18-1009.pdf
Video:
 https://vimeo.com/238231888