  This paper presents a novel approach for modeling threaded discussions on
social media using a graph-structured bidirectional LSTM 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.
